Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis

Table 4 Summary of artificial intelligence in colorectal diseases

Disease	Application	Ref.	Study design	Country/region	Modality	Test set	AI model	Main findings
Ulcerative colitis	Diagnosis	Sutton et al[99]	R	Norway	Endoscopy	8000 images	CNN	AI (especially DenseNet121 model) accurately distinguishes UC from non-UC pathology (AUC 0.999) and grades endoscopic activity (mild/severe, AUC 0.90)
	Diagnosis	Lo et al[100]	R	Denmark	WLI	1484 images	CNN	The CNN model achieved 84% accuracy in distinguishing UC endoscopic severity (Mayo score 0-3), significantly outperforming existing models and standardizing clinical assessment
	Diagnosis	Ruan et al[101]	R	China	Colonoscope	1772 patients	CNN	AI model detects UC/CD at 99.1% accuracy vs physicians' 78%-92.2%, enhancing clinical efficiency
	Diagnosis	Gutierrez Becker et al[102]	R	Europe et al	Endoscopy	1672 videos	CNN	This model directly analyzes raw colonoscopy videos, automatically assessing UC severity (MCES) with high accuracy (AUC 0.84-0.85), reducing manual annotation needs
	Treatment	Bossuyt et al[103]	P	Belgium, Japan	Endoscopy	100 patients	ML	The RD algorithm objectively evaluates UC endoscopic and histologic activity, closely correlated with RHI (r = 0.74), and is sensitive for monitoring therapeutic response
	Treatment	Iacucci et al[104]	P	Europe, North America	HD-WLE, VCE	283 patients	CNN	AI system accurately differentiates UC endoscopic activity/remission (AUC 0.94) and predicts histological remission (83% accuracy), comparable to physicians
	Prognosis	Huang et al[105]	R	China	Colonoscope	856 images	DL, ML	DL/ML-CAD diagnoses mucosal healing (MES 0-1) with 94.5% accuracy and complete healing (MES 0) at 89.0%
	Prognosis	Popa et al[106]	R	Romania	Colonoscope	55 patients	ML	ML models accurately predict endoscopic disease activity one year after anti-TNFα therapy in UC patients (90% accuracy in test set, 100% in validation set)
	Prognosis	Takenaka et al[107]	P	Japan	Endoscopy	2012 patients	DNN	DNN achieves 90.1% accuracy for endoscopic remission and 92.9% for histological remission (UC), reducing biopsy needs
	Prognosis	Maeda et al[108]	P	Japan	Endocytoscope, NBI	145 patients	ML	Real-time AI endoscopy predicts relapse risk in UC remission by analyzing mucosal microvessels (AI-Active 28.4% vs AI-Healing 4.9%, P < 0.001)
Colorectal polyps	Diagnosis	Wang et al[109]	R	China	Colonoscope	1600 patients	CNN	Enhanced GAP model achieves > 98% accuracy (TPR > 96%, TNR > 98%) for colon polyp detection with reduced parameters, enabling lightweight yet accurate diagnosis
	Diagnosis	Song et al[89]	P & R	South Korea	NBI	1169 samples	DL	CAD with NBI predicts polyp histology at 81.3%-82.4% accuracy, outperforming junior physicians (63.8-71.8%) and matching experts (82.4-87.3%), enhancing junior diagnostic performance
	Diagnosis	Jin et al[110]	P & R	South Korea	NBI	2450 images	CNN	AI assistance significantly boosts endoscopists' (especially novices') accuracy for small polyps (< 5 mm) (73.8%→85.6%) and reduces time (3.92→3.37 seconds/polyp)
	Diagnosis	Sakamoto et al[111]	R	Japan	WLI, LCI, BLI	1788 images	DL	CADe sensitive > 94% (WLI/LCI), CADx accuracy > 93% (WLI/BLI), rivals expert endoscopists
	Diagnosis	Zachariah et al[112]	R	USA	WLI, NBI	6223 images	CNN	CNN real-time prediction of colorectal polyp pathology meets PIVI standards: 97% adenoma NPV, > 93% surveillance interval concordance
	Treatment	Wickstrøm et al[113]	R	Europe	Colonoscope	912 images	FCNs	CNNs rely on polyp shape/edges for segmentation; error risk rises significantly in uncertain areas. FCNs combine uncertainty with interpretability visualization, helping doctors pinpoint high-risk regions fast
	Treatment	Su et al[114]	P	China	Colonoscope	659 patients	DCNN	AQCS significantly boosts adenoma detection (28.9% vs 16.5%, P < 0.001), polyp detection, and optimizes withdrawal time and bowel prep during colonoscopy
CRC	Diagnosis	Luo et al[115]	P & R	China	Liquid biopsy	3315 patients	ML	Plasma ctDNA methylation markers (e.g., cg10673833) enable early CRC diagnosis (AUC 0.96) and high-risk group screening (Sensitivity 89.7%)
	Diagnosis	Arabameri et al[116]	R	France, USA, Austria	Fecal microbiota analysis	350 patients	ML	Combining GRNN and DBFS (new feature selection) identified 6 key microbial markers (e.g., Clostridium), enabling high-precision CRC detection (AUC 0.911) but insufficient adenoma sensitivity (AUC 0.724)
	Diagnosis	Zeng et al[117]	P	USA	OCT	26000 images	CNN	PR-OCT system using OCT and RetinaNet distinguishes CRC from normal tissue in real-time with high accuracy (sensitivity 100%, specificity 99.7%, AUC 0.998)
	Treatment	Wang et al[118]	R	China	MRI	240 patients	Faster R-CNN	Faster R-CNN detects positive CRM on rectal cancer pre-op high-resolution MRI with 93.2% accuracy (AUC 0.953); 0.2 seconds/image, highly feasible and efficient
	Treatment	Yang et al[119]	R	China	MRI	89 patients	ML	Pre-treatment ADC radiomics predicts locally advanced rectal cancer resistance to neoadjuvant chemoradiotherapy (AUC 0.83/91.3% accuracy)
	Treatment	Fu et al[120]	R	USA	MRI	43 patients	DL	DL-based radiomics significantly outperform handcrafted features in predicting neoadjuvant chemoradiotherapy response for locally advanced rectal cancer (AUC 0.73 vs 0.64)
	Prognosis	Xu et al[121]	R	China	CT, MRI et al	999 patients	ML	GradientBoosting and LightGBM effectively predict stage IV CRC recurrence risk (AUC up to 0.881); key factors: Chemotherapy, age, LogCEA, CEA, anesthesia duration
	Prognosis	Zhao et al[122]	R	China	Clinical data	7205 patients	ML	ML-based NCDB nomogram predicts metastatic rectal cancer 3-year OS (C-index > 0.77, internal/external validation), outperforming prior models
	Prognosis	Reichling et al[123]	R	France	IHC, WSI	1018 patients	ML	DGMuneS integrating tumor-stroma/CD3+/tumor features better predicts stage III colon cancer recurrence than traditional immune scores (C-index 0.601 vs 0.578)
	Prognosis	Skrede et al[124]	R	Norway, United Kingdom	H&E staining	2467 patients	CNN	Developed a DL-based prognostic biomarker (DoMore v1-CRC) using only routine H&E-stained slides, effectively stratifying Stage II/III CRC risk and outperforming existing markers
	Prognosis	Väyrynen et al[125]	P	USA	H&E staining	1504 samples	ML	ML on H&E slides links dense stromal lymphocytes/eosinophils and their peri-tumoral localization to significantly improved CRC-specific survival

R: Retrospective; P: Prospective; RHI: Robarts Histological Index; RD: Red density; HD-WLE: High definition white light endoscopy; VCE: Virtual chromoendoscopy; TPR: True positive rate; TNR: True Negative Rate; PIVI: Preservation and incorporation of valuable endoscopic innovations; NPV: Negative predictive value; FCNs: Fully convolution networks; AQCS: Automatic quality control system; GRNN: Generalized regression neural network; DBFS: Derivative-based feature selection; OCT: Optical coherence tomography; R-CNN: Faster R-convolutional neural network; CRM: Circumferential resection margin; ADC: Apparent diffusion coefficient; OS: Overall survival; WSI: Whole slide imaging; IHC: Immunohistochemistry; H&E: Hematoxylin & eosin; CRC: Colorectal cancer.