Table 2.

Summary of the included studies and their characteristics

Authors	Bitewing origin	Patient demographic	Intraoral X-ray unit	Image receptor	Exposure parameters
a Included studies on the origin of the retrieved bitewing radiographs, patient demographics, radiographic system, image receptor used, and exposure parameters
Ayhan (2024) [14]	Kırıkkale University	Age: >18	Gendex Expert DC (Gendex Dental Systems, ILB, USA)	–	65 kVp, 7 mA
	Faculty of Dentistry
	Turkey
Karakuş (2024) [26]	Necmettin Erbakan University, Faculty of Dentistry, Turkey	–	Morita Veraview X Type R Intraoral Digital Imaging Device (J. Morita Corp., Kyoto, Japan)	Phosphor plates: Soredex Digora Optime image scanner system from Finland	70 kVp, 7 mA, 0.25 s
de Frutos (2024) [23]	Six Norwegian institutes and research centres, Norway	56% female 44% male	–	–	–
		Age: 19–94
Azhari (2023) [15]	King Abdulaziz University Dental Hospital in Jeddah and dental practitioner, Saudi Arabia	–	–	–	–
Ahmed (2023) [12]	King Abdulaziz University Dental Hospital in Jeddah, Saudi Arabia	–	–	–	–
Panyarak (2022) [31]	Chiang Mai University, Faculty of Dentistry, Thailand	Permanent teeth	Heliodent Plus (Dentsply Sirona, Charlotte, NC, USA)	Carestream 7600 Phosphor Plate (Carestream, Rochester, NY, USA) and VistaScan Imaging Plates Plus (DÜRR DENTAL, Bietigheim-Bissingen, Germany)	–
Kunt (2023) [27]	A hospital information system (not specified)	Adult	–	Three direct radiography and one indirect radiography (manufacture not specified)	–
Vimalarani (2022) [34]	A faculty (not specified)	–	–	–	–
Mao (2021) [29]	–	–	–	–	–
Moran (2021) [30]	–	–	Sirona Heliodent Plus oral X-ray unit (Kavo Brasil Focus)	The EXPRESS™ Origo imaging plate (KaVo Dental, Biberach an der Riss, Germany)	70 kVp, 7 mA, 0.25–0.64 s
Bayrakdar (2022) [18]	Ordu University, School of Dentistry, Turkey	Age: >18	Kodak CS 2200 (Carestream Dental, Atlanta, GA)	2 different phosphor plate systems: Carestream CS 7600(Carestream Dental, Atlanta, GA) and Kodak CR (Carestream Dental, Atlanta, GA)	50 kVp, 5 mA, 0.1 s
Forouzeshfar (2024) [25]	Samin Oral and Maxillofacial Radiology Center in Tehran, Iran	–	Planmeca periapical device, Helsinki, Finland	–	60 kVp, 8 mA, 0.128 s
Cantu (2020) [20]	Dental clinic at Charité-Universitätsmedizin, Berlin, Germany	52% male	Orthophos XG, Dentsply Sirona (Bensheim, Germany), and Dürr Dental (Bietigheim-Bissingen, Germany)	–	–
		48% female
		Mean age: 36.5
		Age: 14–89
Chaves (2024) [21]	Dutch dental practice-based research network, The Netherlands	–	–	–	–
Chen (2022) [22]	Peking University School and Hospital of Stomatology in Beijing, China	–	Gendex Expert DC system (Gendex Dental Systems, Hatfield, PA, USA)	Storage phosphor plate (manufacture not specified)	65 kVp, 7 mA, 0.125–0.32 s
Bayraktar (2022) [19]	Kirikkale University, Faculty of Dentistry, Turkey	Permanent teeth	Gendex Expert DC (Gendex Dental Systems, IL, USA)	–	65 kVp, 7 mA
Panyarak (2023) [32]	Chiang Mai University, Faculty of Dentistry, Thailand	Permanent teeth	Heliodent Plus (Dentsply Sirona, Charlotte, NC, USA)	Carestream 7600 Phosphor Plate (Carestream, Rochester, NY, USA) and VistaScan Imaging Plates Plus (DÜRR DENTAL, Bietigheim-Bissingen, Germany)	–
Estai (2022) [24]	Dental clinics in Perth, Australia	Age: 18–60	Australia	Photostimulable phosphor plate (not specified)	Australia
Lee (2021) [28]	Yonsei University, Conservative Dentistry, Korea	Permanent teeth	Kodak RVG 6200 digital radiography system with CS 2200 (Carestream, Rochester, NY, USA)	–	–
Baydar (2023) [17]	Eski¸sehir Osmangazi University, Faculty of Dentistry, Turkey	–	ProX periapical X-ray unit (Planmeca, Helsinki, Finland)	ProScanner Phosphor Plates and Scanning System (Planmeca, Helsinki, Finland)	60 kVp, 2 mA, 0.05 s
van Nistelrooij (2024) [33]	Dutch dental practice-based research network, The Netherlands	15–88 years	–	–	–
		202 females, 179 males, and 2 unknown
Ayan (2024) [13]	Kirikkale University, Faculty of Dentistry, Turkey	Not considered	Gendex Expert DC (Gendex Dental Systems, IL, USA)	–	65 kVp, 7 mA
Bayati (2025) [16]	Tehran University of Medical Science, Faculty of Dentistry, Iran	Permanent teeth	Owandy dental X-ray machine	Photostimulable phosphor plates, and a Sordex Digora scanner	60 kVp, 6 mA, 0.32 s

Authors	AI models	Images, n, training/total	Annotators, n/ experience	Lesion prevalence	Lesion type	Diagnostic performance of AI models
b Included studies on model architecture, dataset characteristics, annotation details, lesion prevalence in the training model, and diagnostic performance metrics of the AI models
Ayhan (2024) [14]	YOLOv7-AP-CBAM	1,000/1,170	2/>4 years	0.24	Interproximal	F1: 0.82, recall: 0.83, precision: 0. 87
Karakuş (2024) [26]	YOLOv8	−/860	2/>3 years	–	Overall caries	F1: 0.95, recall: 0.93, precision: 0.98
					Interproximal
					D1	F1: 0.96
					D2	F1: 0.96
					D3	F1: 0.93
					Occlusal	F1: 0.95
					Secondary caries	F1: 0.96
de Frutos (2024) [23]	YOLOv5 RetinaNet EfficientDet (D0 and D1)	8,342/8,539	6/–	–	Interproximal	YOLOv5 performed best, except for the F1 for enamel caries (EfficientDet D1)
					Enamel	F1: 0.53, AP: 0.60 (at IoU 0.3), FNR: 0.15
					Dentine	F1: 0.59, AP: 0.62 (at IoU 0.3), FNR: 0.22
					Secondary lesions	F1: 0.56, AP: 0.68 (at IoU 0.3), FNR: 0.16
Azhari (2023) [15]	Ensemble U-Net (ResNet50, ResNext101, VGG19)	–/771	3/–	–	Interproximal adult
					Sound	IoU: 0.98
					Primary	IoU: 0.23
					Moderate	IoU: 0.19
					Advanced	IoU: 0.51
					Overall	Mean F1: 0.52, mean IoU: 0.54
					Interproximal Ped
					Sound	IoU: 0.97
					Primary	IoU: 0.08
					Moderate	IoU: 0.17
					Advanced	IoU: 0.25
					Overall	Mean F1: 0.44, mean IoU: 0.38
Ahmed (2023) [12]	U-Net ensemble (ResNet50, ResNext101, Inception-ResNet-v2)	443/554	2/–	–	Overall caries	Mean F1: 0.54, mean IoU: 0.55
					Modified-ICIDAS system
					Sound	IoU: 0.96
					Outer 1/2 enamel	IoU: 0.21
					Inner 1/2 enamel	IoU: 0.23
					Outer 1/2 dentine	IoU: 0.35
					Inner 1/2 dentine	IoU: 0.41
Panyarak (2022) [31]	YOLOv3	994/1,425	3/>7	0.42	ICCMS™ 2 class	At IoU 0.50
					Overall caries	Precision 0.77, recall: 0.82, F1: 0.79, AP:0.72
				0.34	ICCMS™ 4 class	At IoU 0.50
				0.035	Initial stage	Precision: 0.75, recall: 0.67, F1: 0.71, AP: 0.57
				0.046	Moderate stage	Precision: 0.62, recall: 0.53, F1: 0.57, AP: 0.36
					Extensive stage	Precision: 0.75, recall: 0.78, F1: 0.76, AP: 0.67
				0.08	ICCMS™ 7 class	At IoU 0.50
				0.12	Outer 1/2 enamel	Precision: 0.35, recall: 0.23, F1: 0.27, AP: 0.10
				0.13	Inner 1/2 enamel	Precision: 0.48, recall: 0.63, F1: 0.55, AP: 0.38
				0.035	Outer 1/3 dentine	Precision: 0.55, recall: 0.55, F1: 0.55, AP: 0.37
				0.026	Mid 1/3 dentine	Precision: 0.48, recall: 0.56, F1: 0.52, AP: 0.36
				0.02	Inner 1/3 dentine	Precision: 0.52, recall: 0.61, F1: 0.56, AP:0.39
					Pulp	Precision: 0.77, recall: 0.61, F1: 0.68, AP:0.52
Kunt (2023) [27]	YOLOv5 RetinaNet	2,992/3,989	1/5	–	Overall caries	Ensemble models
	Faster R-CNN EfficientDet
						F1: 0.79–0.80, Precision: 0.81–0.83
						Recall: 0.77–0.79, AP: 0.84–0.86 (at IoU 0.5)
						Individual models
						F1: 0.72–0.76, Precision: 0.71–0.77
						Recall: 0.71–0.79, AP: 0.74–0.83 (at IoU 0.5)
					Small lesion	Individual models at IoU 0.5
						AP: 0.66–0.79
					Medium lesion	AP: 0.78–0.85
					Large lesion	AP: 0.67–0.85
Vimalarani (2022) [34]	DG-LeNet	800/1,000	–/–	0.16	Overall caries	Accuracy: 0.99
						Sensitivity: 0.91, specificity: 0.99
					Premolars	Accuracy: 0.99
					Molars	Sensitivity: 0.93, specificity: 0.99
						Accuracy: 0.99
						Sensitivity: 0.92, specificity: 0.99
Mao (2021) [29]	AlexNet	195/278	3/>3	0.023	Overall caries	Accuracy: 0.90 (AlexNet)
	(GoogleNet					Accuracy: 0.87 (Google Net)
	Vgg19					Accuracy: 0.80 (Vgg19)
	ResNet50)					Accuracy: 0.83 (Resnet50)
Moran (2021) [30]	Inception	–/112	1/–	0.24	Interproximal	The inception model with a 0.001 learning rate performed best
	ResNet				Incipient	Precision: 0.72, recall: 0.87
						Specificity: 0.83, NPV: 0.93, AUC: 0.86
				0.13	Interproximal	Precision: 0.69, recall: 0.733
					Advanced	Specificity: 0.83, NPV: 0.86, AUC: 0.81
Bayrakdar (2022) [18]	VGG-16	–/621	2/>9	–	Overall caries	VGG-16 (detection)
						Sensitivity: 0.84, precision: 0.84, F1: 0.84
						U-Net (segmentation)
						Sensitivity: 0.81, precision: 0.86, F1: 0.84
					Overall caries	VGG-16 (detection)
					External dataset	Sensitivity: 0.77, precision: 0.78, F1: 0.78
						U-Net (segmentation)
						Sensitivity: 0.76, precision: 0.87, F1: 0.81
Forouzeshfar (2024) [25]	VGG16, VGG19, AlexNet, ResNet50	–/1,517	2/–	–	Overall caries	VGG19 performed best
						Accuracy: 0.91–0.94
						F1: 0.90–0.93, precision: 0.90–0.93
						Sensitivity: 0.91–0.95, specificity: 0.89–096
Cantu (2022) [20]	U-Net with EfficientNet-B5 encoder	3,293/3,686	4/>3 years	–	Overall caries	Accuracy: 0.80
						F1: 0.73
						Sensitivity: ≥0.70
Chaves (2024) [21]	Mask-RCNN with Swin Transformer backbone	340/425	2/2+3*	–	Interproximal	Interproximal
					Primary caries	Sensitivity: 0.74, precision: 0.68
						F1: 0.69; AUC: 0.81
					Secondary caries	Sensitivity: 0.70, precision: 0.75
						F1: 0.72; AUC: 0.80
Chen (2022) [22]	Faster R-CNN	818/978	3/>5 years	0.25	Interproximal	Accuracy: 0.87, F1: 0.74, precision: 0.77
					All caries	Sensitivity: 0.72, specificity: 0.93
					E1, E2	Sensitivity: 0.65, precision: 0.69
					D1	Sensitivity: 0.69, precision: 0.71
					D2 and D3	Sensitivity: 0.85, precision: 0.92
Bayraktar (2022) [19]	Modified YOLOv3	800/1,000	2/>10 years	0.16	Interproximal caries	Accuracy: 0.95, AUC: 0.87
						Sensitivity: 0.72, specificity: 0.98
						Precision: 0.87, NPV: 0.97
Panyarak (2023) [32]	ResNet-18 ResNet-50	2,333/2,758	3/>7	0.32–0.34	Overall caries	ResNet-50 performed best
	ResNet-101			0.035–0.036	ICCMS™ 4 class	Accuracy: 0.70–0.75, AUC: 0.88–0.91
	ResNet-152			0.046–0.047	Initial stage	Sensitivity: 0.78–0.83
					Moderate stage	Specificity: 0.54–0.67
					Extensive stage
				0.08	Overall caries	ResNet-152 performed best
				0.12	ICCMS™ 7 class	Accuracy: 0.53–0.62
				0.13	Outer 1/2 enamel	AUC: 0.84–0.87
				0.035	Inner 1/2 enamel	Sensitivity: 0.63–0.75
				0.026	Outer 1/3 dentine	Specificity: 0.30–0.34
				0.02	Middle 1/3 dentine
					Inner 1/3 dentine
					Pulp
Estai (2022) [24]	Faster R-CNN Inception-ResNet-v2	−/2,468	3/−	0.38	Interproximal	Accuracy: 0.87, F1: 0.87, recall: 0.89 precision: 0.86, specificity: 0.86
Lee (2021) [28]	U-Net	−/304	2/>5	–	Overall caries	Precision: 0.63, sensitivity: 0.65, F1: 0.64
Baydar (2023) [17]	U-Net	400/500	2/>3	–	Overall caries	Sensitivity: 0.82, precision: 0.95, F1: 0.88
Van Nistelrooij (2024) [33]	Mask-RCNN with Swin Transformer backbone	330/413	2/5	0.15	Secondary caries	AUC: 0.95, F1 score: 0.77, precision: 0.81
			4			Sensitivity: 0.74, specificity: 0.97
Ayan (2024) [13]	Modified YOLOv5	1,000/1,200	2/>10 years	0.26	Overall caries	mAP: 0.63–0.65
				0.06	All caries	mAP: 0.52–0.54
				0.20	Enamel caries	mAP: 0.74–0.77
					Dentine caries
Bayati (2025) [16]	YOLOv8	442/552	1/–	–	Interproximal caries	Precision: 0.84, recall: 0.80, F1: 0.82, FNR: 0.20
			1/>10 years		All caries	Precision: 0.96, recall: 0.96, F1: 0.96, FNR: 0.04
					Enamel caries	Precision: 0.80, recall: 0.73, F1: 0.77, FNR: 0.27
					Dentine caries

A dash (“–”) indicates missing information.

A star (“*”) indicates that cases with annotator discrepancies were reviewed by additional annotators.

F1, F1 score; AUC, area under the curve; FPR, false-negative rate; NPV, negative predictive value; AP, average precision; mAP, mean average precision; IoU, intersection over union.