Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders

Table 1 Performance of machine learning models for diagnosis, risk stratification, and prognosis in esophageal diseases

Ref.	ML classifier	Disorder	Outcomes	Data type (n)	Performance	Validation (n)
Risk factor screening
Yan et al[70]	SVM	EV	Identify esophageal varices with high bleeding risk	CT image (796), China	AUC = 0.74	External validation (405)
Hong et al[77]	EfficientNet/Grad-CAM	EV	Assess the risk of EV bleeding within 12 months	Endoscopy (675), China	ACC = 91%	External validation (400)
Chen et al[76]	DCNNs	EV	Classify the EV grade to identify patients at high risk of bleeding	Endoscopy (10655), China	ACC = 97%	Independent validation (200)
Gao et al[44]	LightGBM	EC	Screen for carcinoma at the esophagogastric junction	Questionnaire, cytology and endoscopy (17000), China	AUC = 0.96	External validation (2901)
Iyer et al[41]	Transformer	EC	Screen for patients with BE and EC among healthy individuals	Clinical data (260000), United States	AUC = 0.84	Independent validation (10%)
Rosenfeld et al[25]	Logistic regression	BE	Screen for BE and alert patients who may need endoscopy	Questionnaire (1299), United Kingdom	AUC = 0.86	Independent validation (523)
Fockens et al[54]	EfficientNet/MobileNetV2/DeepLabV3 +	BE, EC	Detect and mark lesion sites of BE and EC	Endoscopy (13000), Netherlands	Sensitivity = 88%	Independent validation (200)
Prognosis and survival analysis
Wang et al[75]	DCNNs	EV	Determine size, shape, color and bleeding signs of EV and GV	Endoscopy (17000), China	ACC = 93%	External validation (11000)
Nopour[51]	Random forest	EC	Predict 5-year survival of patients with EC	Clinical data (1656), Iran	AUC = 0.76	External validation (100)
Lu et al[53]	Random forest	EC	Predict 3-year and 5-year survival of patients with EC	Clinical data (2521), China	AUC = 0.77	Independent validation (30%)
Knabe et al[56]	VGG16	EC	Identify BE, early EC, and advanced tumors (T1b sm2, T3, T4)	Endoscopy (1020), Germany	ACC = 73%	Independent validation (199)
Personalized medication
Sasagawa et al[66]	Decision tree	EC	Predict chemotherapy response by immunogenomic features	Genomic and transcriptomic data (121), Japan	ACC = 84%	Independent validation (30%)
Chuwdhury et al[65]	Random forest	EC	Predict neoantigens by multi-omics features	Genomic and transcriptomic data (805), China	AUC = 0.87	Independent validation (211 peptides)
Zhu et al[49]	XGBoost	EC	Analyses differences in G4RIL risk between proton and photon therapies	Clinical data (746), United States	AUC = 0.78	Independent validation (247)
Chu et al[50]	XGBoost and logistic regression	EC	Personalized prediction of G4RIL based on CDS and four clinical risk factors	Clinical data (860), United States	AUC = 0.78	Independent validation (20%)
Clinical phenotype classification
Li et al[26]	ResNeXt50	GERD	Classify the Los Angeles grade of reflux esophagitis	Endoscopy (3498), China	ACC = 90.2%	Independent validation (396)
Jia et al[59]	Consensus clustering	EC	Classify EC into four clusters with BRCA1, PD-1, vascular invasion, and tumor stages	CT image (546), China	P = 0.035	External validation (546)
Chempak Kumar and Mubarak[57]	Artificial bee colony/CNN/SVM	BE, ESCC, EAC	Detect and analyze BE, EAC, and ESCC	Endoscopy (1028), India	ACC = 97%	3-fold cross-validation
Takahashi et al[69]	Hierarchical clustering	Achalasia	Classify achalasia into three clusters, according to Chicago classification	Demography, clinical data and endoscopy (1824), Japan	AUC: 0.61-0.7	External validation (1824)
Carlson et al[68]	Decision tree	Achalasia	Classify achalasia and distinguish spastic from non-spastic types	HRM data (180), United States	ACC = 78%	Independent validation (40)
Faghani et al[40]	YOLOv5	BE	Classify BE into NDBE, LGD, and HGD	Histology (542), United States	ACC = 81.3%	Independent validation (70)
Clinical decision support system
Rogers et al[28]	Decision tree	GERD	Identify MNBI, and diagnose GERD based on MNBI	pH-impedance data (325), United States and Italy	ACC = 88.5%	Independent validation (2049)
Wong et al[30]	ResNet18	GERD	Identify reflux events and calculate the PSPW index	pH-impedance data (106), China	ACC = 87%	Independent validation (10%)
Yen et al[36]	VGG16/RF	GERD	Assess esophageal mucosal damage	Endoscopy (496), China	ACC = 92.5%	Independent validation (32)
Zhou et al[31]	S4 model	GERD	Identify reflux events	pH-impedance data (45), United States	AUC = 0.87	Independent validation (20)
Meng et al[46]	XGBoost	EC	Diagnose ESCC according to the relative abundance of salivary flora	Microbiome (8000), Multisource	ACC = 89.9%	5-fold cross-validation
Rubenstein et al[45]	Logistic regression/decision tree/XGBoost	EC	Distinguish early- and late-stage EC	Clinical data (11400000), United States	AUC: 0.75-0.85	Independent validation (2600000)
Yuan et al[43]	YOLACT	EC	Detect and segment lesions of patients with EC	Endoscopic image (10000) and video (140), China	ACC = 87%	External validation (1141)
Thavanesan et al[47]	Logistic regression, random forests, extreme gradient boosting and decision tree	EC	MDT treatment decisions predict and curative EC	Clinical data (399), United Kingdom	AUC: 0.71-0.79	10-fold cross-validation
Huang et al[48]	Extremely randomized trees	EC	Individualized treatment decisions for elderly patients with inoperable ESCC	Clinical data, CT and endoscopy (189), China	AUC = 0.84	Independent validation (20%)
Li et al[55]	eUNet	EC	Segment lesions in EC endoscopic images	Endoscopy (2848), Multisource	Dice = 0.89	Independent validation (300)

AUC: Area under the curve; ACC: Accuracy; BE: Barrett’s esophagus; CDS: Composite dosimetric score; CNN: Convolutional neural network; DCNNs: Diffusion-convolutional neural networks; Dice: Sørensen-Dice coefficient; EAC: Esophageal adenocarcinoma; EC: Esophageal carcinoma; ESCC: Esophageal squamous cell carcinoma; eUNet: edge-enhanced UNet; EV: Esophageal varices; G4RIL: Grade 4 radiotherapy-induced lymphopenia; GERD: Gastroesophageal reflux disease; HGD: High-grade dysplasia; LGD: Low-grade dysplasia; LightGBM: Light gradient boosting machine; MNBI: Mean nocturnal baseline impedance; NDBE: Nondysplastic Barrett’s esophagus; PD-1: Programmed death-1; PSPW: Post-reflux swallow-induced peristaltic wave; ResNet: Residual neural network; ResNeXt: Residual networks with next; RF: Random forest; S4 model: Structured state space sequence model; SVM: Support vector machine; VGG: Visual geometry group; XGBoot: EXtreme gradient boosting; YOLACT: You only look at coefficients; YOLO: You Only Look Once; MDT: Multidisciplinary team; CT: Computed tomography; pH: Potential of hydrogen.