Gynecologic cancers affect over 1.2 million women globally each year. Early diagnosis and effective treatment are essential for improving patient outcomes, yet traditional diagnostic methods often encounter limitations, particularly in low-resource settings. Artificial intelligence (AI) has emerged as a transformative tool that enhances accuracy and efficiency across various aspects of gynecologic oncology, including screening, diagnosis, and treatment. This review examines the current applications of AI in gynecologic cancer care, focusing on areas such as early detection, imaging, personalized treatment planning, and patient monitoring. Based on an analysis of 75 peer-reviewed articles published between 2017 and 2024, we highlight AI's contributions to cervical, ovarian, and endometrial cancer management. AI has notably improved early detection, achieving up to 95% accuracy in cervical cancer screening through AI-enhanced Pap smear analysis and colposcopy. For ovarian and endometrial cancers, AI-driven imaging and biomarker detection have enabled more personalized treatment approaches. In addition, AI tools have enhanced precision in robotic-assisted surgery and radiotherapy, and AI-based histopathology has reduced diagnostic variability. Despite these advancements, challenges such as data privacy, bias, and the need for human oversight must be addressed. The successful integration of AI into clinical practice will require careful consideration of ethical issues and a balanced approach that incorporates human expertise. Overall, AI presents significant potential to improve outcomes in gynecologic oncology, particularly in bridging healthcare gaps in resource-limited settings.

Background: The advent of artificial intelligence (AI) has revolutionised many fields in healthcare. More recently, it has garnered interest in terms of its potential applications in histopathology, where algorithms are increasingly being explored as adjunct technologies that can support pathologists in diagnosis, molecular typing and prognostication. While many research endeavours have focused on solid tumours, gynaecological malignancies have nevertheless been relatively overlooked. The aim of this review was therefore to provide a summary of the status quo in the field of AI in gynaecological pathology by encompassing malignancies throughout the entirety of the female reproductive tract rather than focusing on individual cancers. Methods: This narrative/scoping review explores the potential application of AI in whole slide image analysis in gynaecological histopathology, drawing on both findings from the research setting (where such technologies largely remain confined), and highlights any findings and/or applications identified and developed in other cancers that could be translated to this arena. Results: A particular focus is given to ovarian, endometrial, cervical and vulval/vaginal tumours. This review discusses different algorithms, their performance and potential applications. Conclusions: The effective application of AI tools is only possible through multidisciplinary co-operation and training.

The expression of nicotinamide-phosphoribosyl transferase (NAMPT) was demonstrated to increase in various dysplastic and malignant conditions, usually consistent with the severity of the disease. This study was conducted to assess the utility of extracellular NAMPT (eNAMPT) in the management of cervical dysplasia in human papillomavirus (HPV) infected women. Circulating eNAMPT concentrations in high-risk HPV-infected women who were diagnosed with high-grade squamous intraepithelial lesion (HSIL) or invasive cancer (cervical intraepithelial neoplasia 2+ (CIN2+) lesions) and who were revealed to have no cervical dysplasia or low-grade squamous intraepithelial lesion (LSIL) were evaluated and compared. One hundred fifty nine high-risk HPV-infected patients for cervical biopsies under colposcopy guidance between February 2022 and February 2023 were included in this case-control study. Study group composed of consecutively enrolled 84 women with histological diagnosis of HSIL or cervical cancer (CIN2+ lesions) and control group composed of consecutively enrolled 75 women with LSIL or normal cervical biopsies. Circulating eNAMPT concentrations of cases with CIN2+ lesions and cases with LSIL or normal cervical biopsies were compared. No significant difference was found between median peripheral venous blood eNAMPT concentration of cases with histologic diagnosis of CIN2+ lesions and cases with LSIL or normal cervical biopsies (9.4 ng/mL (0.19-192) vs 8.9 ng/mL (0.19-176.9); p = 0.07, respectively). Multivariate linear regression analysis revealed no independent predictor of circulating eNAMPT concentrations among possible predictor variables. In conclusion, circulating eNAMPT concentrations of cases with CIN2+ lesions and cases with LSIL or normal cervical biopsies were found to be similar. Further research that evaluates cervical fluid eNAMPT concentrations might define novel noninvasive tools in cervical dysplasia management.

Accurate histopathological evaluation of pancreatic ductal adenocarcinoma (PDAC), including primary tumor lesions and lymph node metastases, is critical for prognostic evaluation and personalized therapeutic strategies. Distinct from other solid tumors, PDAC presents unique diagnostic challenges owing to its extensive desmoplasia, unclear tumor boundary, and difficulty in differentiating from chronic pancreatitis. These characteristics not only complicate pathological diagnosis but also hinder the acquisition of pixel-level annotations required for training computational pathology models. In this study, we present PANseg, a multiscale weakly supervised deep learning framework for PDAC segmentation, trained and tested on 368 whole-slide images (WSIs) from 208 patients across 2 independent centers. Using only image-level labels (2048 × 2048 pixels), PANseg achieved comparable performance with fully supervised baseline (FSB) across the internal test set 1 (17 patients/58 WSIs; PANseg area under the receiver operating characteristic curve [AUROC]: 0.969 vs FSB AUROC: 0.968), internal test set 2 (40 patients/44 WSIs; PANseg AUROC: 0.991 vs FSB AUROC: 0.980), and external test set (20 patients/20 WSIs; PANseg AUROC: 0.950 vs FSB AUROC: 0.958). Moreover, the model demonstrated considerable generalizability with previously unseen sample types, attaining AUROCs of 0.878 on fresh-frozen specimens (20 patients/20 WSIs) and 0.821 on biopsy sections (20 patients/20 WSIs). In lymph node metastasis detection, PANseg augmented the diagnostic accuracy of 6 pathologists from 0.888 to 0.961, while reducing the average diagnostic time by 32.6% (72.0 vs 48.5 minutes). This study demonstrates that our weakly supervised model can achieve expert-level segmentation performance and substantially reduce annotation burden. The clinical implementation of PANseg holds great potential in enhancing diagnostic precision and workflow efficiency in the routine histopathological assessment of PDAC.

Objective: We compared the diagnostic performance of artificial intelligence (AI) with that of a gynecologic oncologist during digital cervicography. Methods: Women with abnormal cytology who underwent cervicography between January 2019 and December 2023 were included. A gynecologic oncologist interpreted the digital cervicography and the results were compared with those of the AI system. Diagnostic performances were assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for low-grade squamous intraepithelial lesions (LSILs) and high-grade squamous intraepithelial lesions (HSILs)/cancer. Cohen's kappa quantified agreement. Results: This study included 449 women (mean age, 41.0 years). A Cohen's kappa of 0.511 (p < 0.0001) indicated moderate agreement between the oncologist and AI. Among 226 cases of HSILs/cancer, the oncologist's sensitivity was 62.8%, compared to 47.8% for AI, with similar specificity (81.2% vs. 83.5%). The oncologist's PPV and NPV were 85.0% and 56.3%, respectively, whereas AI's were 83.1% and 48.5%, respectively. For LSILs/HSILs/cancer (n = 283), the oncologist achieved 98.2% sensitivity and 44.7% specificity, compared to AI's 93.3% sensitivity and 46.1% specificity. Both had a similar PPV (86.9% vs. 86.6%); however, the oncologist's NPV (87.2%) exceeded AI's 64.8%. Diagnostic accuracy for LSILs/HSILs/cancer was 86.9% for the oncologist and 82.3% for AI, whereas for HSILs/cancer, it was 69.6% and 61.0%, respectively. Conclusions: Moderate agreement was observed between the oncologist and AI. Although AI demonstrated similar performance in diagnosing cervical lesions, the oncologist achieved higher diagnostic accuracy. AI is a complementary tool and future research should refine AI algorithms to align with clinical performance.

Cervical cancer burden is disproportionately higher in low to middle income countries, especially in countries with a high human immunodeficiency virus (HIV) burden. This review investigates barriers to implementation and assesses current progress in cervical cancer screening in lower resource settings by reviewing technologies and strategies that have already been implemented in low to middle income countries.

Several novel innovations embrace the recent World Health Organization (WHO) update to screening guidelines that recommends a "screen and treat" approach rather than a "screen, triage and treat" approach. Innovations include human papillomavirus (HPV) self-sampling, portable cervical visualization devices, and creative large-scale approaches to increase screening accessibility. Overall, a low-cost, accurate, point-of-care screening test could alleviate most of the barriers associated with cervical cancer screening in lower resource settings. Further research into the development of a low-cost HPV test in conjunction with the HPV vaccine and other screening tools could expedite progress.

Literature shows that women's sexual behavior, specifically, engagement in multiple sexual partnerships (MSP) has some association with the risk of developing cervical cancer. In the context of the Kenyan demographic and health survey, MSP is defined as having more one sexual partner excluding spouse, in last 12 months. Yet, it is unclear how engagement in MSP independently predicts women's uptake of cervical cancer screening (CCS). The study examined the association between recent MSP and CCS uptake among women of reproductive age in Kenya.

Data of 16,824 women aged 15-49 who participated in the 2022 Kenya demographic and health survey was used. Recent MSP was defined as having more than one sexual partner, excluding spouse, in last 12 months. The analysis was carried out in STATA version 18. Chi-square tests, bivariable and multivariable logistic regression were performed. The adjusted odds ratio from the multivariable logistic regression were reported along with the 95% confidence interval (CI).

The analysis shows that only 16.68% of the sampled women (i.e., 2,837 out of a total sample of 16,824) had ever been screened for cervical cancer by a healthcare professional. In the bivariable analysis, women who were involved in MSP were more likely [OR = 1.20; 95%CI: 1.07-1.34] to undergo screening for cervical cancer compared to those not involved in MSP. This association remains significant after adjusting for confounders [AOR = 1.34; 95%CI: 1.19-1.52].

The low screening rate in Kenya is concerning given the importance of early detection in improving cervical cancer outcomes. The study concludes that recent engagement in MSP is significantly associated with women's uptake of CCS. The study further concludes that there is a need for public health campaigns to raise awareness about the importance of cervical cancer screening among all women, regardless of their sexual behavior. Educational initiatives must emphasize that cervical cancer screening is crucial for all women, not just those with MSP.

Cervical cancer is the second most prominent cancer among women in Bangladesh, which is mainly caused by persistent infection with high-risk human papillomavirus (hrHPV). This study aims to evaluate impact of hrHPV prevalence on cost-effectiveness of screening with self-sampling hrHPV testing versus visual inspection with acetic acid (VIA) for cervical cancer screening in low- and middle-income countries with Bangladesh as an example.

A micro-simulation Markov model was developed from a health system perspective in Bangladesh to evaluate the cost-effectiveness of screening with self-sampling hrHPV testing followed by VIA and VIA as primary screening method followed by colposcopy. We compared these strategies in optimal (70%) and realistic (8.7%) uptake scenarios, considering different hrHPV prevalence rates. Key indicators for cost-effectiveness were number of prevented cervical cancers cases and incremental cost-effectiveness ratio (ICER).

The number of cervical cancers cases prevented by screening and cost-effectiveness of screening strategies increased as hrHPV prevalence increased. In both optimal and realistic uptake scenarios, hrHPV test + VIA strategy prevented more cancers than VIA + colposcopy strategy in most instances. Regardless of the uptake, both screening strategies were cost-effective compared to no screening within a hrHPV prevalence range of 2-30%, and the hrHPV test-based strategy was cost-effective compared with VIA-based strategy. When the price of hrHPV test was estimated 50% lower (10 USD), the hrHPV test-based strategy gained more life years at nearly the same cost as the VIA-based strategy.

Our study demonstrates that the hrHPV test + VIA strategy is cost-effective both compared to no screening and VIA + colposcopy screening strategy under the optimal (70%) and realistic (8.7%) uptake scenarios, with greater cost-effectiveness at higher hrHPV prevalence levels. While VIA-based strategy is cheaper, self-sampling hrHPV test-based strategy offers greater health benefits. Implementing hrHPV testing in national screening programs at lower hrHPV test prices is crucial for promoting health equity and accelerating cervical cancer elimination worldwide. In resource-constrained settings, screening with hrHPV testing should initially target high-prevalence populations.

Cervical cytology screening and colposcopy play crucial roles in cervical intraepithelial neoplasia (CIN) and cervical cancer prevention. Previous studies have provided evidence that artificial intelligence (AI) has remarkable diagnostic accuracy in these procedures. With this systematic review and meta-analysis, we aimed to examine the pooled accuracy, sensitivity, and specificity of AI-assisted cervical cytology screening and colposcopy for cervical intraepithelial neoplasia and cervical cancer screening.

In this systematic review and meta-analysis, we searched the PubMed, Embase, and Cochrane Library databases for studies published between January 1, 1986 and August 31, 2024. Studies investigating the sensitivity and specificity of AI-assisted cervical cytology screening and colposcopy for histologically verified cervical intraepithelial neoplasia and cervical cancer and a minimum of five cases were included. The performance of AI and experienced colposcopists was assessed via the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) through random effect models. Additionally, subgroup analyses of multiple diagnostic performance metrics in developed and developing countries were conducted. This study was registered with PROSPERO (CRD42024534049).

Seventy-seven studies met the eligibility criteria for inclusion in this study. The pooled diagnostic parameters of AI-assisted cervical cytology via Papanicolaou (Pap) smears were as follows: accuracy, 94% (95% CI 92-96); sensitivity, 95% (95% CI 91-98); specificity, 94% (95% CI 89-97); PPV, 88% (95% CI 78-96); and NPV, 95% (95% CI 89-99). The pooled accuracy, sensitivity, specificity, PPV, and NPV of AI-assisted cervical cytology via ThinPrep cytologic test (TCT) were 90% (95% CI 85-94), 97% (95% CI 95-99), 94% (95% CI 85-98), 84% (95% CI 64-98), and 96% (95% CI 94-98), respectively. Subgroup analysis revealed that, for AI-assisted cervical cytology diagnosis, certain performance indicators were superior in developed countries compared to developing countries. Compared with experienced colposcopists, AI demonstrated superior accuracy in colposcopic examinations (odds ratio (OR) 1.75; 95% CI 1.33-2.31; P < 0.0001; I2 = 93%).

These results underscore the potential and practical value of AI in preventing and enabling early diagnosis of cervical cancer. Further research should support the development of AI for cervical cancer screening, including in low- and middle-income countries with limited resources.

This study was supported by the National Natural Science Foundation of China (No. 81901493) and the Shanghai Pujiang Program (No. 21PJD006).

The proportion of women dying from cervical cancer in middle- and low-income countries is over 60%, twice that of their high-income counterparts. A primary screening strategy to eliminate this burden is cervix visualization and application of 3-5% acetic acid, inducing contrast in potential lesions. Recently, machine learning tools have emerged to aid visual diagnosis. As low-cost visualization tools expand, it is important to maximize image quality at the time of the exam or of images used in algorithms.

We present the use of an object detection algorithm, the YOLOv5 model, to localize the cervix and describe blur within a multi-device image database.

We took advantage of the Fourier domain to provide pseudo-labeling of training and testing images. A YOLOv5 model was trained using Pocket Colposcope, Mobile ODT EVA, and standard of care digital colposcope images.

When tested on all devices, this model achieved a mean average precision score, sensitivity, and specificity of 0.9, 0.89, and 0.89, respectively. Mobile ODT EVA and Pocket Colposcope hold out sets yielded mAP score of 0.81 and 0.83, respectively, reflecting the generalizability of the algorithm. Compared to physician annotation, it yielded an accuracy of 0.72.

This method provides an informed quantitative, generalizable analysis of captured images that is highly concordant with expert annotation.

This quality control framework can assist in the standardization of colposcopy workflow, data acquisition, and image analysis and in doing so increase the availability of usable positive images for the development of deep learning algorithms.

To understand whether human papillomavirus (HPV) and cytological testing (TCT [ie, "thinprep" cytological testing]) results can provide more information beyond visual information for vaginal colposcopy examinations to upgrade or downgrade the visual diagnosis of vaginal colposcopy.

Data from 519 patients, who underwent vaginal colposcopy at the Beijing Obstetrics and Gynecology Hospital (Beijing, China) between January and June 2020, were included. Preoperative HPV and TCT results were statistically analyzed, and were divided into 3 groups according to postoperative cervical tissue pathological diagnosis: negative; low-grade squamous intraepithelial (LSIL); and high-grade squamous intraepithelial lesion (HSIL). Positive and negative predictive values for cervical inflammation, LSIL, and HSIL in patients diagnosed using vaginal colposcopy, based on cervical pathological grouping, and differences in HPV and TCT results among patients who underwent vaginal colposcopy, were analyzed.

The age of patients diagnosed with cervicitis, LSIL, and HSIL using colposcopy gradually decreased, and the proportion of HPV16/18 infection in the HSIL group was significantly higher than the other 2 groups. There were significant differences in TCT results among the groups. According to pathological results from cervical tissue specimens, among all groups diagnosed using colposcopy, the age of the HSIL group was significantly younger than that of the other groups, and the proportion of patients with a TCT greater than LSIL was significantly higher than that of the other groups.

HPV did not provide additional information for vaginal colposcopy. Young(er) patients and those with a TCT greater than LSIL may consider upgrading the vaginal colposcopy diagnosis based on imaging information.

Tumor heterogeneity significantly complicates the selection of effective cancer treatments, as patient responses to drugs can vary widely. Personalized cancer therapy has emerged as a promising strategy to enhance treatment effectiveness and precision. This study aimed to develop a personalized drug recommendation model leveraging genomic profiles to optimize therapeutic outcomes.

A content-based filtering algorithm was implemented to predict drug sensitivity. Patient features were characterized by the tumor microenvironment (TME), and drug features were represented by drug fingerprints. The model was trained and validated using the Genomics of Drug Sensitivity in Cancer (GDSC) database, followed by independent validation with the Cancer Cell Line Encyclopedia (CCLE) dataset. Clinical application was assessed using The Cancer Genome Atlas (TCGA) dataset, with Best Overall Response (BOR) serving as the clinical efficacy measure. Two multilayer perceptron (MLP) models were built to predict IC50 values for 542 tumor cell lines across 18 drugs.

The model exhibited high predictive accuracy, with correlation coefficients (R) of 0.914 in the training set and 0.902 in the test set. Predictions for cytotoxic drugs, including Docetaxel (R = 0.72) and Cisplatin (R = 0.71), were particularly robust, whereas predictions for targeted therapies were less accurate (R < 0.3). Validation with CCLE (MFI as the endpoint) showed strong correlations (R = 0.67). Application to TCGA data successfully predicted clinical outcomes, including a significant association with 6-month progression-free survival (PFS, P = 0.007, AUC = 0.793).

The model demonstrates strong performance across preclinical datasets, showing its potential for real-world application in personalized cancer therapy. By bridging preclinical IC50 and clinical BOR endpoints, this approach provides a promising tool for optimizing patient-specific treatments.

Adequate knowledge and awareness regarding diseases are essential for appropriate, high-quality healthcare. Female Genital Schistosomiasis (FGS) is a non-sexually transmitted gynaecological disease that is caused by the presence of Schistosoma haematobium eggs in the female genital tract and the resulting immune response that causes tissue damage. It is estimated to affect 56 million women, mostly in sub-Saharan Africa (SSA), where healthcare workers (HCWs) have limited awareness and knowledge of FGS. Most migrants in Europe are female, often from SSA and therefore at risk of FGS. This study investigated awareness and knowledge of FGS among European HCWs with the aim of informing strategies to improve the management of migrant health.

We conducted a cross-sectional survey using a self-administered, closed, multilingual, anonymous online questionnaire between 1st June 2023 to 31st January 2024. Medical doctors (MDs) (n = 581) and nurses or midwives (NMs) (n = 341) working in infectiology, gynaecology, urology and general, travel, internal or occupational medicine in European countries were enrolled in the survey. A Poisson regression was used to identify factors associated with MDs' knowledge and awareness of FGS and adjusted prevalence ratios (aPR) were estimated. Practices related to FGS were described using counts and proportions for a subsample of MDs aware of FGS.

Among the 922 eligible participants, FGS awareness was 43.7% (CI95%: 39.6; 47.9) for MDs and 12.0% (CI95%: 8.8; 16.0) for NMs. FGS awareness among MDs was higher among men (50.0%; CI95%: 43.7; 56.3), working in clinics for migrants (72.0%, CI95%: 63.2; 79.7) and among infectiologists/travel medicine specialists (68.9%, CI95%: 62.2; 75.0). No knowledge was reported by 67.6% (95% CI 63.7-71.4) of MDs, while 25.3% (CI95%: 21.8; 29.0) had low and 7.1% (CI95%: 5.1; 9.5) medium knowledge. Working in healthcare for migrants was positively associated with medium knowledge (aPR = 3.49; CI95% 1.67;7.28), which was lower for general practitioners (aPR = 0.23, CI95%:0.07;0.81).

Our study highlights that HCWs in Europe might not be adequately prepared to manage FGS patients, resulting in a high risk of neglect. We believe that the promotion of existing medical networks could improve knowledge about FGS and thus the health of migrant women.

The value of the transformation zone (TZ) is often overlooked in clinical settings. This study aims to assess TZ distribution, associated factors, and its impact on colposcopic diagnosis.

χ 2 tests were used to analyze demographics, clinical history, and tissue samples to examine the differences in TZ distribution. Factors affecting the TZ were explored using logistic regression, and diagnostic indicators were calculated.

A total of 5,302 individual datasets were finally included. TZ1, TZ2, and TZ3 accounted for 31.6%, 38.5%, and 30.0%, respectively. Age is the most important factor that influences the location of the TZ. The proportion of TZ3 steadily increased with age, comprising over 55% in women over 50. The colposcopic diagnostic performance shows that high-grade squamous intraepithelial lesion or worse (HSIL+) sensitivity of TZ3 (58.1%, 95% confidence interval [CI] = 52.9-63.4) is significantly lower than that of TZ1 (69.8%, 95% CI = 65.5-74.1) and TZ2 (73.2%, 95% CI = 69.7-76.8). The HSIL+ specificity of TZ3 (96.3, 95% CI = 95.3-97.4) was higher than that of TZ1 (96.3, 95% CI = 95.2-97.3) and TZ2 (92.5, 95% CI = 91.1-93.9). The HSIL+ positive predictive value (81.3%, 95% CI = 76.4-86.2) and negative predictive value (89.3%, 95% CI = 87.6-90.9) for TZ3 are high, with no significant differences when compared with TZ1 and TZ2.

Age predominantly influences TZ location, with TZ3 being most frequently found in women over 50. While TZ3 poses a higher risk of missed diagnosis during colposcopy, it remains clinically valuable in identifying diseased and nondiseased status. Increasing colposcopists' awareness of TZ importance is needed in clinical practice.

The World Health Organization has set a global strategy to eliminate cervical cancer, emphasizing the need for cervical cancer screening coverage to reach 70%. In response, China has developed an action plan to accelerate the elimination of cervical cancer, with Hubei province implementing China's first provincial full-coverage screening program using an artificial intelligence (AI) and cloud-based diagnostic system.

This study aimed to evaluate the performance of AI technology in this full-coverage screening program. The evaluation indicators included accessibility, screening efficiency, diagnostic quality, and program cost.

Characteristics of 1,704,461 individuals screened from July 2022 to January 2023 were used to analyze accessibility and AI screening efficiency. A random sample of 220 individuals was used for external diagnostic quality control. The costs of different participating screening institutions were assessed.

Cervical cancer screening services were extended to all administrative districts, especially in rural areas. Rural women had the highest participation rate at 67.54% (1,147,839/1,699,591). Approximately 1.7 million individuals were screened, achieving a cumulative coverage of 13.45% in about 6 months. Full-coverage programs could be achieved by AI technology in approximately 1 year, which was 87.5 times more efficient than the manual reading of slides. The sample compliance rate was as high as 99.1%, and compliance rates for positive, negative, and pathology biopsy reviews exceeded 96%. The cost of this program was CN ¥49 (the average exchange rate in 2022 is as follows: US $1=CN ¥6.7261) per person, with the primary screening institution and the third-party testing institute receiving CN ¥19 and ¥27, respectively.

AI-assisted diagnosis has proven to be accessible, efficient, reliable, and low cost, which could support the implementation of full-coverage screening programs, especially in areas with insufficient health resources. AI technology served as a crucial tool for rapidly and effectively increasing screening coverage, which would accelerate the achievement of the World Health Organization's goals of eliminating cervical cancer.

Colposcopy is a medical procedure for detecting cervical lesions. Access to devices required for colposcopy procedures is limited in low- and middle-income countries. However, various existing digital imaging techniques based on artificial intelligence offer solutions to analyze colposcopy images and address accessibility challenges.

We systematically searched PubMed, National Library of Medicine, and Crossref, which met our inclusion criteria for our study. Various methods and research gaps are addressed, including how variability in images and sample size affect the accuracy of the methods. The quality and risk of each study were assessed following the QUADAS-2 guidelines.

Development of image analysis and compression algorithms, and their efficiency are analyzed. Most of the studied algorithms have attained specificity, sensitivity, and accuracy which range from 86% to 95%, 75%-100%, and 100%, respectively, and these results were validated by the clinician to analyze the images quickly and thus minimize biases among the clinicians.

This systematic review provides a comprehensive study on colposcopy image analysis stages and the advantages of utilizing digital imaging techniques to enhance image analysis and diagnostic procedures and ensure prompt consultations. Furthermore, compression techniques can be applied to send medical images over media for further analysis among periphery hospitals.

The risk associated with single and multiple human papillomavirus (HPV) infections in cervical intraepithelial neoplasia (CIN) remains uncertain. This study aims to explore the distribution and diagnostic significance of the number of high-risk HPV (hr-HPV) infections in detecting CIN, addressing a crucial gap in our understanding. This comprehensive multicenter, retrospective study meticulously analyzed the distribution of single and multiple hr-HPV, the risk of CIN2+, the relationship with CIN, and the impact on the diagnostic performance of colposcopy using demographic information, clinical histories, and tissue samples. The composition of a single infection was predominantly HPV16, 52, 58, 18, and 51, while HPV16 and 33 were identified as the primary causes of CIN2+. The primary instances of dual infection were mainly observed in combinations such as HPV16/18, HPV16/52, and HPV16/58, while HPV16/33 was identified as the primary cause of CIN2+. The incidence of hr-HPV infections shows a dose-response relationship with the risk of CIN (p for trend <0.001). Compared to single hr-HPV, multiple hr-HPV infections were associated with increased risks of CIN1 (1.44, 95% confidence interval [CI]: 1.20-1.72), CIN2 (1.70, 95% CI: 1.38-2.09), and CIN3 (1.08, 95% CI: 0.86-1.37). The colposcopy-based specificity of single hr-HPV (93.4, 95% CI: 92.4-94.4) and multiple hr-HPV (92.9, 95% CI: 90.8-94.6) was significantly lower than negative (97.9, 95% CI: 97.0-98.5) in detecting high-grade squamous intraepithelial lesion or worse (HSIL+). However, the sensitivity of single hr-HPV (73.5, 95% CI: 70.8-76.0) and multiple hr-HPV (71.8, 95% CI: 67.0-76.2) was higher than negative (62.0, 95% CI: 51.0-71.9) in detecting HSIL+. We found that multiple hr-HPV infections increase the risk of developing CIN lesions compared to a single infection. Colposcopy for HSIL+ detection showed high sensitivity and low specificity for hr-HPV infection. Apart from HPV16, this study also found that HPV33 is a major pathogenic genotype.

Primary cervical cancer screening and treating precancerous lesions are effective ways to prevent cervical cancer. However, the coverage rates of human papillomavirus (HPV) vaccines and routine screening are low in most developing countries and even some developed countries. This study aimed to explore the benefit of an artificial intelligence-assisted cytology (AI) system in a screening program for a cervical cancer high-risk population in China.

A total of 1231 liquid-based cytology (LBC) slides from women who underwent colposcopy at the Chinese PLA General Hospital from 2018 to 2020 were collected. All women had received a histological diagnosis based on the results of colposcopy and biopsy. The sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), false-positive rate (FPR), false-negative rate (FNR), overall accuracy (OA), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and Youden index (YI) of the AI, LBC, HPV, LBC + HPV, AI + LBC, AI + HPV and HPV Seq LBC screening strategies at low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL) thresholds were calculated to assess their effectiveness. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic values of the different screening strategies.

The Se and Sp of the primary AI-alone strategy at the LSIL and HSIL thresholds were superior to those of the LBC + HPV cotesting strategy. Among the screening strategies, the YIs of the AI strategy at the LSIL + threshold and HSIL + threshold were the highest. At the HSIL + threshold, the AI strategy achieved the best result, with an AUC value of 0.621 (95% CI, 0.587-0.654), whereas HPV testing achieved the worst result, with an AUC value of 0.521 (95% CI, 0.484-0.559). Similarly, at the LSIL + threshold, the LBC-based strategy achieved the best result, with an AUC of 0.637 (95% CI, 0.606-0.668), whereas HPV testing achieved the worst result, with an AUC of 0.524 (95% CI, 0.491-0.557). Moreover, the AUCs of the AI and LBC strategies at this threshold were similar (0.631 and 0.637, respectively).

These results confirmed that AI-only screening was the most authoritative method for diagnosing HSILs and LSILs, improving the accuracy of colposcopy diagnosis, and was more beneficial for patients than traditional LBC + HPV cotesting.

Background: Cervical cancer is the fourth most common cancer among women worldwide. Cervical cancer usually develops from human papillomavirus (HPV) infection, which leads to cervical intraepithelial neoplasia (CIN1/2/3) and eventually invasive cervical cancer. Therefore, early-screening and detection of cervical lesions are crucial for preventing and treating cervical cancer. However, different regions have different levels of medical resources and availability of diagnostic methods. There is a need to compare the efficiency of different methods and combinations for detecting cervical lesions and provide recommendations for the optimal screening and detection strategies. Methods: The current clinical methods for screening and detection of cervical lesions mainly include TruScreen (TS), Thinprep cytologic test (TCT), HPV testing, and colposcopy, but their sensitivity and specificity vary and there is no standard protocol recommended. In this study, we retrospectively reviewed 2286 female samples that underwent cervical biopsy and compared the efficiency of different methods and combinations for detecting cervical lesions. Results: HPV screening showed the highest sensitivity for identifying women with CIN2+ cervical lesions compared with other single methods. Our results also showed the importance and necessary of the secondary diagnostic test like TCT and TS as a triage method before colposcopy examination and guided biopsy. Conclusions: Our study provides recommendations for the optimal screening and detection strategies for cervical lesions in different regions with different levels of development. As a non-invasive, easily operated, and portable device, TS is a promising tool to replace TCT for detecting cervical lesions in the health care center with insufficient medical resources.

Cervical cancer is a major health concern worldwide, highlighting the urgent need for better early detection methods to improve outcomes for patients. In this study, we present a novel digital pathology classification approach that combines Low-Rank Adaptation (LoRA) with the Vision Transformer (ViT) model. This method is aimed at making cervix type classification more efficient through a deep learning classifier that does not require as much data. The key innovation is the use of LoRA, which allows for the effective training of the model with smaller datasets, making the most of the ability of ViT to represent visual information. This approach performs better than traditional Convolutional Neural Network (CNN) models, including Residual Networks (ResNets), especially when it comes to performance and the ability to generalize in situations where data are limited. Through thorough experiments and analysis on various dataset sizes, we found that our more streamlined classifier is highly accurate in spotting various cervical anomalies across several cases. This work advances the development of sophisticated computer-aided diagnostic systems, facilitating more rapid and accurate detection of cervical cancer, thereby significantly enhancing patient care outcomes.

The intersection of artificial intelligence (AI) with cancer research is increasing, and many of the advances have focused on the analysis of cancer images.

To describe and synthesize the literature on the diagnostic accuracy of AI in early imaging diagnosis of cervical cancer following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR).

Arksey and O'Malley methodology was used and PubMed, Scopus, and Google Scholar databases were searched using a combination of English and Spanish keywords.

Identified titles and abstracts were screened to select original reports and cross-checked for overlap of cases.

A descriptive summary was organized by the AI algorithm used, total of images analyzed, data source, clinical comparison criteria, and diagnosis performance.

We identified 32 studies published between 2009 and 2022. The primary sources of images were digital colposcopy, cervicography, and mobile devices. The machine learning/deep learning (DL) algorithms applied in the articles included support vector machine (SVM), random forest classifier, k-nearest neighbors, multilayer perceptron, C4.5, Naïve Bayes, AdaBoost, XGboots, conditional random fields, Bayes classifier, convolutional neural network (CNN; and variations), ResNet (several versions), YOLO+EfficientNetB0, and visual geometry group (VGG; several versions). SVM and DL methods (CNN, ResNet, VGG) showed the best diagnostic performances, with an accuracy of over 97%.

We concluded that the use of AI for cervical cancer screening has increased over the years, and some results (mainly from DL) are very promising. However, further research is necessary to validate these findings.

Colposcopy plays an essential role in diagnosing cervical lesions and directing biopsy; however, there are few studies of the capabilities of colposcopists in medically underserved communities in China. This study aims to fill this gap by assessing colposcopists' competencies in medically underserved communities of China.

Colposcopists in medically underserved communities across China were considered eligible to participate. Assessments involved presenting participants with 20 cases, each consisting of several images and various indications. Participants were asked to determine transformation zone (TZ) type, colposcopic diagnoses and to decide whether biopsy was necessary. Participants are categorized according to the number of colposcopic examinations, i.e., above or below 50 per annum.

There were 214 participants in this study. TZ determination accuracy was 0.47 (95% CI 0.45,0.49). Accuracy for colposcopic diagnosis was 0.53 (95% CI 0.51,0.55). Decision to perform biopsies was 0.73 accurate (95% CI 0.71,0.74). Participants had 0.61 (95% CI 0.59,0.64) sensitivity and a 0.80 (95% CI 0.79,0.82) specificity for detecting high-grade lesions. Colposcopists who performed more than 50 cases were more accurate than those performed fewer across all indicators, with a higher sensitivity (0.66 vs. 0.57, p = 0.001) for detecting high-grade lesions.

In medically underserved communities of China, colposcopists appear to perform poorly at TZ identification, colposcopic diagnosis, and when deciding to biopsy. Colposcopists who undertake more than 50 colposcopies each year performed better than those who perform fewer. Therefore, colposcopic practice does improve through case exposure although there is an urgent need for further pre-professional and clinical training.

To investigate age and type-specific prevalences of high-risk human papillomavirus (hrHPV) and cervical intraepithelial neoplasia (CIN) in hrHPV+ women referred to colposcopy. This is a retrospective, multicenter study. Participants were women referred to one of seven colposcopy clinics in China after testing positive for hrHPV. Patient characteristics, hrHPV genotyping, colposcopic impressions, and histological diagnoses were abstracted from electronic records. Main outcomes were age-related type-specific prevalences associated with hrHPV and CIN, and colposcopic accuracy. Among 4419 hrHPV+ women referred to colposcopy, HPV 16, 52, and 58 were the most common genotypes. HPV 16 prevalence was 39.96%, decreasing from 42.57% in the youngest group to 30.81% in the eldest group. CIN3+ prevalence was 15.00% and increased with age. As lesion severity increases, HPV16 prevalence increased while the prevalence of HPV 52 and 58 decreased. No age-based trend was identified with HPV16 prevalence among CIN2+, and HPV16-related CIN2+ was less common in women aged 60 and above (44.26%) compared to those younger than 60 years (59.61%). Colposcopy was 0.73 sensitive at detecting CIN2+ (95% confidence interval[CI]: 0.71, 0.75), with higher sensitivity (0.77) observed in HPV16+ women (95% CI: 0.74, 0.80) compared to HPV16- women (0.68, 95% CI: 0.64, 0.71). Distributions of hrHPV genotypes, CIN, and type-specific CIN in Chinese mainland hrHPV+ women referred to colposcopy were investigated for the first time. Distributions were found to be age-dependent and colposcopic performance appears related to HPV genotypes. These findings could be used to improve the management of women referred to colposcopy.

Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.

Implementation of high-risk human papillomavirus (hrHPV) screening has greatly reduced the incidence and mortality of cervical cancer. However, a triage strategy that is effective, noninvasive, and independent from the subjective interpretation of pathologists is urgently required to decrease unnecessary colposcopy referrals in hrHPV-positive women.

A total of 3251 hrHPV-positive women aged 30-82 years (median = 41 years) from International Peace Maternity and Child Health Hospital were included in the training set (n = 2116) and the validation set (n = 1135) to establish Cervical cancer Methylation (CerMe) detection. The performance of CerMe as a triage for hrHPV-positive women was evaluated.

CerMe detection efficiently distinguished cervical intraepithelial neoplasia grade 2 or worse (CIN2 +) from cervical intraepithelial neoplasia grade 1 or normal (CIN1 -) women with excellent sensitivity of 82.4% (95% CI = 72.6 ~ 89.8%) and specificity of 91.1% (95% CI = 89.2 ~ 92.7%). Importantly, CerMe showed improved specificity (92.1% vs. 74.9%) in other 12 hrHPV type-positive women as well as superior sensitivity (80.8% vs. 61.5%) and specificity (88.9% vs. 75.3%) in HPV16/18 type-positive women compared with cytology testing. CerMe performed well in the triage of hrHPV-positive women with ASC-US (sensitivity = 74.4%, specificity = 87.5%) or LSIL cytology (sensitivity = 84.4%, specificity = 83.9%).

PCDHGB7 hypermethylation-based CerMe detection can be used as a triage strategy for hrHPV-positive women to reduce unnecessary over-referrals.

ChiCTR2100048972. Registered on 19 July 2021.

Ninety percent of cervical cancer (CC) diagnoses and deaths occur in low and middle-income countries (LMICs). Especially in these countries, where human and material resources are limited, there is a need for real-time screening methods that enable immediate treatment decisions (i.e., 'see and treat').

To evaluate whether handheld vital microscopy (HVM) enables real-time detection of microvascular alterations associated with cervical intraepithelial neoplasia (CIN) and CC.

A cross-sectional study was conducted in an oncologic hospital and outpatient clinic, and included ten healthy controls, ten women with CIN, and ten women with CC. The microvasculature was assessed in four quadrants of the uterine cervix using HVM. The primary outcome was the presence of abnormal angioarchitecture (AA). Secondary outcomes included capillary loop density (CD), total vessel density (TVD), functional capillary density (FCD), and the proportion of perfused vessels (PPV).

198 image sequences of the cervical microvasculature were recorded. Compared to healthy controls, significantly more abnormal image sequences were observed in women with high-grade CIN (11 % vs. 44 %, P < 0.001) and women with CC (11 % vs. 69 %, P < 0.001). TVD, FCD, and PPV were lower in women with CIN and CC.

HVM enables easy, real-time, non-invasive assessment of cervical lesions through the detection of microvascular alterations. Thereby, HVM potentially provides an opportunity for point-of-care screening, which may enable immediate treatment decisions (see and treat) and reduce the number of unnecessary surgical interventions.

The objective of this study was to assess the attitudes toward the Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) of colposcopists working in mainland China.

A questionnaire was developed to collect participants' sociodemographic information and assess their awareness, attitudes, and acceptance toward the CAIADS.

There were 284 respondents from 24 provinces across mainland China, with 55% working in primary care institutions. Participant data were divided into two subgroups based on their colposcopy case load per year (i.e. ≥50 cases; <50 cases). The analysis showed that participants with higher loads had more experience working with CAIADS and were more knowledgeable about CAIADS and AI systems. Overall, in both groups, about half of the participants understood the potential applications of big data and AI-assisted diagnostic systems in medicine. Although less than one-third of the participants were knowledgeable about CAIADS and its latest developments, more than 90% of the participants were open with the idea of using CAIADS.

While a related lack of acknowledgement of CAIADS exists, the participants in general had an open attitude toward CAIADS. Practical experience with colposcopy or CAIADS contributed to participants' awareness and positive attitudes. The promotion of AI tools like CAIADS could help address regional health inequities to improve women's well-being, especially in low- and middle-income countries.

Cervical cancer is one of the most dangerous and widespread illnesses afflicting women throughout the globe, particularly in East Africa and South Asia. In industrialised nations, the incidence of cervical cancer has consistently decreased over the past few decades. However, in developing countries, the reduction in incidence has been considerably slower, and in some instances, the incidence has increased. Implementing routine screenings for cervical cancer is something that has to be done to protect the health of women. Cervical cancer is famously difficult to diagnose and cure due to the slow rate at which it spreads and develops into more advanced stages of the disease. Screening for cervical cancer using a Pap smear, more often referred to as a Pap test, has the potential to detect the illness in its earlier stages. For the purpose of selecting features for this article, a gray level co-occurrence matrix (GLCM) technique was used. Following this step, classification is performed with methods such as convolutional neural network (CNN), support vector machine, and auto encoder. According to the findings of this experiment, the GLCM-CNN classifier proved to be the one with the highest degree of precision.

Cancer is the leading cause of death worldwide. Early detection of cancer can lower the mortality of all types of cancer; however, effective early-detection biomarkers are lacking for most types of cancers. DNA methylation has always been a major target of interest because DNA methylation usually occurs before other detectable genetic changes. While investigating the common features of cancer using a novel guide positioning sequencing for DNA methylation, a series of universal cancer only markers (UCOMs) have emerged as strong candidates for effective and accurate early detection of cancer. While the clinical value of current cancer biomarkers is diminished by low sensitivity and/or low specificity, the unique characteristics of UCOMs ensure clinically meaningful results. Validation of the clinical potential of UCOMs in lung, cervical, endometrial, and urothelial cancers further supports the application of UCOMs in multiple cancer types and various clinical scenarios. In fact, the applications of UCOMs are currently under active investigation with further evaluation in the early detection of cancer, auxiliary diagnosis, treatment efficacy, and recurrence monitoring. The molecular mechanisms by which UCOMs detect cancers are the next important topics to be investigated. The application of UCOMs in real-world scenarios also requires implementation and refinement.

Semantic segmentation is crucial in medical image diagnosis. Traditional deep convolutional neural networks excel in image classification and object detection but fall short in segmentation tasks. Enhancing the accuracy and efficiency of detecting high-level cervical lesions and invasive cancer poses a primary challenge in segmentation model development.

Between 2018 and 2022, we retrospectively studied a total of 777 patients, comprising 339 patients with high-level cervical lesions and 313 patients with microinvasive or invasive cervical cancer. Overall, 1554 colposcopic images were put into the DeepLabv3+ model for learning. Accuracy, Precision, Specificity, and mIoU were employed to evaluate the performance of the model in the prediction of cervical high-level lesions and cancer.

Experiments showed that our segmentation model had better diagnosis efficiency than colposcopic experts and other artificial intelligence models, and reached Accuracy of 93.29 %, Precision of 87.2 %, Specificity of 90.1 %, and mIoU of 80.27 %, respectively.

The DeepLabv3+ model had good performance in the segmentation of cervical lesions in colposcopic post-acetic-acid images and can better assist colposcopists in improving the diagnosis.

As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.

Colposcopy is a cornerstone of cervical cancer prevention; however, there is a global shortage of colposcopists. It is challenging to train a sufficient number of colposcopists through in-person methods, which hinders our ability to adequately diagnose and manage positive cases. A digital platform is needed to make colposcopy training more efficient, scalable, and sustainable; however, current online training programs are generally based on didactic curricula that do not incorporate image analysis training. In addition, long-term assessments of online training are not readily available. Therefore, innovative digital training and an assessment of its effectiveness are needed.

This study aimed to evaluate the short- and long-term effects of DECO (an online Digital Education Tool for Colposcopy) on trainees' colposcopy competencies and confidence.

DECO can be used both on laptops and smartphones and comprises 4 training modules (image interpretation; terminology learning; video teaching; and collection of guidelines and typical cases) and 2 test modules. DECO was tested through a pre-post study between September and November 2022. Participants were recruited in China, and DECO training lasted 12 days. Trainees initially learned basic theory before completing training using 200 image-based cases. Pretest, posttest, and follow-up testing included 20 distinct image-based questions, and was conducted on Days 0, 13, and 60. Primary outcomes were competence and confidence scores. Secondary measures were response distributions for colposcopic diagnoses, biopsies, and DECO training satisfaction. Multilevel modeling was used to determine improvement from baseline to posttraining and follow-up for the outcomes of interest.

Among 402 participants recruited, 96.8% (n=389) completed pretesting, 84.1% (n=338) posttesting, and 75.1% (n=302) follow-up testing. Colposcopic competence and confidence increased across this study. Diagnostic scores improved on average from 55.3 (53.7-56.9) to 70.4 (68.9-71.9). The diagnostic accuracy for normal/benign lesions, low-grade squamous intraepithelial lesions, and high-grade squamous intraepithelial lesions or worse increased by 16.9%, 13.1%, and 16.9%, respectively. Mean confidence scores increased from 48.1 (45.6-50.6) to 56.2 (54.5-57.9). These improvements remained evident 2 months after training. Trainees were also satisfied with DECO overall. Most found DECO to be scientific (82.5%), easy to use (75.2%), and clinically useful (98.4%), and would recommend it to colleagues (93.2%).

DECO is a useful, acceptable digital education tool that improves colposcopy competencies and confidence. DECO could make colposcopy training more efficient, scalable, and sustainable because there are no geographic or time limitations. Therefore, DECO could be used to alleviate the shortage of trained colposcopists around the world.

Female genital schistosomiasis (FGS) is a neglected disease with long-term physical and psychosocial consequences, affecting approximately 50 million women worldwide and generally representing an unmet medical need on a global scale. FGS is the chronic manifestation of a persistent infection with Schistosoma haematobium. FGS services are not routinely offered in endemic settings with a small percentage of women at risk receiving adequate care. Madagascar has over 60% prevalence of FGS and no guidelines for the management of the disease. This study aimed to determine FGS knowledge among women and health care workers (HCWs) in a highly endemic area of Madagascar.

A convenience sampling strategy was used for this cross-sectional study. Descriptive statistics including proportions and 95% confidence intervals (CI) were calculated, reporting socio-demographic characteristics of the population. Knowledge sources were evaluated descriptively. Binary Poisson regression with robust standard errors was performed; crude (CPR) and adjusted prevalence ratio (APR) with 95% CIs were calculated.

A total of 783 participants were included in the study. Among women, 11.3% (n = 78) were aware of FGS while among the HCWs 53.8% (n = 50) were aware of FGS. The highest level of knowledge was observed among women in an urban setting [24%, (n = 31)] and among those with a university education/vocational training [23% (n = 13)]. A lower APR of FGS knowledge was observed in peri-urban [APR 0.25 (95% CI: 0.15; 0.45)] and rural [APR 0.37 (95% CI 0.22; 0.63)] settings in comparison to the urban setting. Most HCWs reported other HCWs [40% (n = 20)] while women mainly reported their family [32% (n = 25)] as being their main source of information in the 6 months prior to the survey.

Our study shows limited awareness and knowledge of FGS among population groups in the highly endemic Boeny region of Madagascar. With this study we contribute to identifying an important health gap in Madagascar, which relates to a disease that can silently affect millions of women worldwide. In alignment with the targets of the NTD roadmap, addressing schistosomiasis requires a paradigm shift for its control and management including a greater focus on chronic forms of the disease.

Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm.

A total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values.

Sensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively.

This study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.

In low-resource settings, a point-of-care test for cervical cancer screening that can give an immediate result to guide management is urgently needed. A transvaginal digital device, "Smart Scope®" (SS), with an artificial intelligence-enabled auto-image-assessment (SS-AI) feature, was developed. In a single-arm observational study, eligible consenting women underwent a Smart Scope®-aided VIA-VILI test. Images of the cervix were captured using SS and categorized by SS-AI in four groups (green, amber, high-risk amber (HRA), red) based on risk assessment. Green and amber were classified as SS-AI negative while HRA and red were classified as SS-AI positive. The SS-AI-positive women were advised colposcopy and guided biopsy. The cervix images of SS-AI-negative cases were evaluated by an expert colposcopist (SS-M); those suspected of being positive were also recommended colposcopy and guided biopsy. Histopathology was considered a gold standard. Data on 877 SS-AI, 485 colposcopy, and 213 histopathology were available for analysis. The SS-AI showed high sensitivity (90.3%), specificity (75.3%), accuracy (84.04%), and correlation coefficient (0.670, p = 0.0) in comparison with histology at the CINI+ cutoff. In conclusion, the AI-enabled Smart Scope® test is a good alternative to the existing screening tests as it gives a real-time accurate assessment of cervical health and an opportunity for immediate triaging with visual evidence.

Women's health in resource-limited settings can benefit from the integrated management of high-burden diseases, such as female genital schistosomiasis (FGS) and human papilloma virus (HPV)-related cervical cancer. In schistosomiasis-endemic countries such as Madagascar, data on FGS and HPV prevalence are lacking as well as preventive measures for both conditions. This study aims to estimate the prevalence of FGS and HPV in rural Madagascar, and to examine associated risk factors to identify opportunities for improving women's health.

After initial community outreach activities, interested women aged 18-49 years were recruited consecutively in 2021 at three primary health care centers in the district of Marovoay. FGS was detected by colposcopy. Colposcopy images were double-blind reviewed by two independent specialists. A Luminex bead-based assay was performed on cervical vaginal lavage specimens for HPV typing. Crude (CPR) and adjusted prevalence ratios (APR) of associations between selected factors and FGS and HPV positivity were estimated using univariable and multivariable binary Poisson regression with 95% confidence intervals (CIs).

Among 500 women enrolled, 302 had complete information on FGS and HPV diagnosis, and were thus eligible for analysis. Within the sample, 189 (62.6%, 95% CI: 56.9-68.1) cases of FGS were detected. A total of 129 women (42.7%, 95% CI: 37.1-48.5) tested positive for HPV. In total, 80 women (26.5%, 95% CI: 21.6-31.8]) tested positive for both conditions. No association was observed between FGS and HPV positivity, while previous pregnancy (APR = 0.65, 95% CI: 0.43-0.78) and older age (APR = 0.59, 95% CI: 0.42-0.81) are showing a negative association with HPV infection compared to no previous pregnancy and younger age groups.

The results of the study show that FGS and HPV are highly prevalent in rural Madagascar. The concurrent prevalence of these two conditions requires urgent adaptations of public health strategies to improve women's health, such as integrated services at primary level of care.

Gastrointestinal (GI) infections are quite common today around the world. Colonoscopy or wireless capsule endoscopy (WCE) are noninvasive methods for examining the whole GI tract for abnormalities. Nevertheless, it requires a great deal of time and effort for doctors to visualize a large number of images, and diagnosis is prone to human error. As a result, developing automated artificial intelligence (AI) based GI disease diagnosis methods is a crucial and emerging research area. AI-based prediction models may lead to improvements in the early diagnosis of gastrointestinal disorders, assessing severity, and healthcare systems for the benefit of patients as well as clinicians. The focus of this research is on the early diagnosis of gastrointestinal diseases using a convolution neural network (CNN) to enhance diagnosis accuracy.

Various CNN models (baseline model and using transfer learning (VGG16, InceptionV3, and ResNet50)) were trained on a benchmark image dataset, KVASIR, containing images from inside the GI tract using n-fold cross-validation. The dataset comprises images of three disease states-polyps, ulcerative colitis, and esophagitis-as well as images of the healthy colon. Data augmentation strategies together with statistical measures were used to improve and evaluate the model's performance. Additionally, the test set comprising 1200 images was used to evaluate the model's accuracy and robustness.

The CNN model using the weights of the ResNet50 pre-trained model achieved the highest average accuracy of approximately 99.80% on the training set (100% precision and approximately 99% recall) and accuracies of 99.50% and 99.16% on the validation and additional test set, respectively, while diagnosing GI diseases. When compared to other existing systems, the proposed ResNet50 model outperforms them all.

The findings of this study indicate that AI-based prediction models using CNNs, specifically ResNet50, can improve diagnostic accuracy for detecting gastrointestinal polyps, ulcerative colitis, and esophagitis. The prediction model is available at https://github.com/anjus02/GI-disease-classification.git.