Utilizing artificial intelligence in endoscopy: a clinician’s guide

Figures & data

Figure 1. Representative images of gastric cancer detected by artificial intelligence.

(a) A reddish and slightly depressed lesion of gastric cancer appears on the greater curvature of the lower gastric body.(b) The yellow rectangular frame was marked by artificial intelligence as a possible lesion to indicate the extent of a suspected gastric cancer lesion. An endoscopist manually marked the location of the cancer using a green rectangular frame. [0–IIc, 10 mm, tub1, T1a(M)].

Figure 2. Representative images of esophageal cancer detected by artificial intelligence.

(a) Reddish irregular area of esophageal squamous cell carcinoma (ESCC) on the right wall in white light imaging. The endoscopist marked the lesion using a green square, and an artificial intelligence (AI) diagnosing system surrounded the lesion using a white square to diagnose esophageal cancer. Because both squares were matched perfectly, we know the AI diagnosing system could detect ESCC correctly.(b) In narrow band imaging, the same lesion appears as a Brownish area. The AI diagnosing system detected ESCC in the same way.

Table 1. Use of artificial intelligence in stomach field.

	Reference (Year)	Application	Procedure	CNN archi-tecture	Training dataset	Validation dataset	AUC	Accuracy (%)	Sensitivity (%)	Specificity (%)
Malignant disease	Hirasawa (2018) [5]	Detection of GC	WLI, CE, NBI	SSD	51,558 images (abnormal 13,584)	Abnormal 2,296 images	n/a	n/a	92.2	n/a
	Ishioka (2019) [6]	Detection of GC	WLI, CE, NBI	SSD	51,558 images (abnormal 13,584)	Abnormal 68 videos	n/a	n/a	94.1	n/a
	Wu (2019) [7]^†	Detection of GC	WLI, CE, NBI	VGG ResNet	9,151 images (abnormal 3,170)	200 images (abnormal 100)	n/a	92.5	94.0	91.0
	Kanesaka (2018) [9]	Diagnosis of early GC	ME-NBI	SVM	126 images (abnormal 66)	81 images (abnormal 61)	n/a	96.3	96.7	95
	Li (2019) [10]	Diagnosis of early GC	ME-NBI	Inception-v3	2088 images (abnormal 1,702)	341 images (abnormal 170)	n/a	90.91	91.18	90.64
	Horiuchi (2019) [11]	Diagnosis of early GC	ME-NBI	GoogLeNet	2,570 images (abnormal 1,492)	258 images (abnormal 151)	0.85	85.3	95.4	71.0
	Zhu (2019) [12]	Diagnosis of invasion depth of GC	WLI	ResNet	abnormal 790 images	abnormal 203 images	0.94	89.16	76.47	95.56
Non-neoplastic disorder	Shichijo (2017) [13]	Diagnosis of H. pylori infection	WLI, CE, NBI	GoogLeNet	32,208 ‣unclassified/‣classified images from 1,768 patients (abnormal 753)	11,481 images from 397 patients (abnormal 72)	‣0.89 ‣0.93	‣83.1 ‣87.7	‣81.9 ‣88.9	‣83.4 ‣87.4
	Itoh (2018) [14]	Diagnosis of H. pylori infection	WLI	GoogLeNet	149 images (abnormal 70)	30 images (abnormal 15)	0.956	n/a	86.7	86.7
	Nakashima (2018) [15]	Diagnosis of H. pylori infection	‣WLI ‣BLI ‣LCI	GoogLeNet	162 images (abnormal 75)	60 images (abnormal 30)	‣0.66 (WLI) ‣0.96 (BLI-bright) ‣0.95 (LCI)	n/a	‣66.7 (WLI) ‣96.7 (BLI-bright) ‣96.7 (LCI)	‣60 (WLI) ‣86.7 (BLI-bright) ‣83.3 (LCI)
	Guimarães (2020) [16]	Diagnosis of H. pylori infection	WLI	ImageNET	200 images (abnormal 100)	70 images (abnormal 30)	0.981	92.9	100	87.5
Anatomical classification of stomach	Takiyama (2018) [18]	Classification of gastric location	WLI	GoogLeNet	27,335 EGD images (Stomach 20,202)	17,081 EGD images (Stomach 13,048)	0.99	n/a	‣96.9 (U-stomach) ‣95.9 (M-stomach) ‣96.0 (L-stomach)	‣98.5 (U-stomach) ‣98.0 (M-stomach) ‣98.8 (L-stomach)
	Wu (2019) [7]^††	Classification of gastric location	WLI	VGG ResNet	24,549 images	170 images	n/a	‣90.0 (into 10 parts) ‣65.9 (into 26 parts)	n/a	n/a
	Wu (2019) [19]	Monitoring blind spots	WLI	VGG DenseNet	34,513 images	107 videos	n/a	Average 90.02 (range for each site 70.21–100)	Average 87.5 (range for each site 63.4–100)	Average 95.02 (range for each site 75–100)

^{†, ††}Same article

Abbreviations: CNN, convolutional neural network; GC, gastric cancer; WLI, white light image; CE, Chromoendoscopy; NBI, narrow band imaging; SSD, Single Shot MultiBox Detector; ME-MBI, magnifying endoscopy with narrow band imaging; H. pylori, helicobacter pylori; BLI, blue light imaging; LCI, linked color imaging; EGD, Esophagogastroduodenoscopy

Table 2. Use of artificial intelligence in colonic polyp.

	Reference (year)	Study design	Training set	Testing set	Output style	AUC	Accuracy (%)	Sensitivity (%)	Specificity (%)	Miscellaneous
CADe systems	Urban 2018 [57]	Retrospective	‣ 8641 static images ‣ 9 videos (video 1) ‣ WLI and NBI ‣ Normal magnification	‣ 1330 static images ‣ 9 videos (= 44947 frames, video 1) ‣ 11 videos (video 2) ‣ WLI and NBI ‣ Normal magnification	Localize	0.974	96.4%	n/a	n/a	CNN overlay led to the identification of 45 polyps – nine more than the 36 identified by experts in video 1, with 81 false positive polyps, and to the identification of 68 polyps vs. 73 identified by experts in video 2, with 46 false positive polyps.
	Misawa 2018 [58]	Retrospective	‣ 73 videos with 155 polyps (= 63,125 polyp positive frames, 133,496 polyp negative frames) were divided into short videos; 2/3 polyp positive short videos and 4/5 polyp negative short videos were used as the training set; the rest were the testing set. ‣ WLI only		Alert on frame	n/a	76.5%	90%	63.3%
	Wang 2018 [59]	Retrospective	‣ 3634 static images with polyps and 1911 static images without polyps	‣ 5541 static images with polyps, and 21,572 static images without polyps (set A) ‣ 29 public videos with polyps (set B) ‣ 54 videos without polyps (set C)	Localize	‣ 0.984 (set A)		‣94.4% (set A) ‣88.2% (image-based analysis) ‣100% (set B) ‣ (set C)	‣95.9% (set A) ‣95.4% (set C)
	Yamada 2019 [60]	Retrospective	‣ 4087 static images of polyps ‣ videos (891 frames of polyps and 134,983 frames of non-cancerous lesions)	‣ 705 static images with polyps, and 4135 static images without polyps ‣ 77 videos	Localize	‣ 0.975 (static images)		‣ 97.3% (static images) ‣ 74% (frame-based analysis)	‣ 99% (static images) ‣ 94.5% (frame-based analysis)	FNR 26.1%, FPR 1.2% in polyp positive videos and FPR 5.5% in polyp negative videos for frame-based analysis
	Becq 2019 [61]	Prospective (Non-real time manner)	Written above (Wang et al. 2018)	50 videos	Localize	n/a	n/a	98.8%	n/a	PPV 40.6% for polyp detection; PDR for the endoscopist was 62% vs. 82% for the CADe system.
	Klare 2019 [62]	Prospective (real-time manner)	Not addressed (“KoloPol” was used)	55 routine colonoscopies	Localize	n/a	n/a	75.3%	n/a	PDRs 50.9% and ADRs 29.1% (55/73) for polyp-based analysis
	Wang 2019 [63]	Prospective randomized	Written above (Wang et al. 2018)	536 with standard CS vs. 522 CS with CAD system	Localize	n/a	n/a	n/a	n/a	ADR was significantly increased in the group with the CAD system (29.1% vs. 20.3%)
	Wang 2020 [64]	Double-blind randomized	Written above (Wang et al. 2018)	478 sham system vs. 484 CAD system	Localize	n/a	n/a	n/a	n/a	ADR was significantly higher in the group with the CAD system (32% vs. 28%)
CADx systems	Byrne 2017 [78]	Retrospective	‣ 223 videos of polyp (= 60,089 frames, 29% NICE type 1, 53% NICE type 2, 18% no polyps) ‣ NBI only ‣ Normal and near focus	40 videos (as a validation set) 125 videos with 51 HPs and 74 ADs (as a testing set)	NICE type 1 vs. type 2	0.95	94%	98%	83%	PPV 90%, and NPV 97% for adenoma
	Komeda 2017 [79]	Retrospective	‣ 1200 images from videos ‣ WLI, NBI, and CE	‣ Cross-validation ‣ 10 videos	Adenomatous vs. Non- adenomatous	n/a	75.1%	n/a	n/a	70% of the polyps were correct in colonoscopy videos.
	Chen 2018 [80]	Prospective (Non-real time manner)	‣ Static images of 1476 NPs and 681 HPs ‣ NBI only	‣ 188 images of NPs and 96 images of HPs ‣ < 5 mm in size ‣ Static image ‣ NBI only	Neoplastic vs. hyperplastic	n/a	90.1%	96.3%	78.1%	PPV 89.6%, and NPV 91.5%
	Song 2020 [81]	Retrospective	‣ 12,480 static images from 624 polyps (48 HPs, 76 SSLs, 393 BAs, 62 MSMCs, 45 DSMCs) ‣ NBI near focus images	‣ 182 polyps (15 HPs, 24 SSLs, 106 BAs, 20 MSMCs, 17 DSMCs) (set 1) ‣ 363 polyps (30 HPs, 70 SSLs, 206 BAs, 28 MSMCs, 29 DSMCs) (set 2)	SSL vs. BA/MSMC vs. DSMC	‣ 0.93 (SSL)(set 1) ‣ 0.86 (BA/MSMC)(set 1) ‣ 0.91 (DSMC)(set 1) ‣ 0.95 (SSL)(set 2) ‣ 0.89 (BA/MSMC)(set 2) ‣ 0.89 (DSMC)(set 2)	81.3% (set 1) 82.4% (set 2)	‣ 82.1% (SSL)(set 1) ‣ 84.1% (BA/MSMC)(set 1) ‣ 58.5% (DSMC)(set 1) ‣ 74.0% (SSL)(set 2) ‣ 88.5% (BA/MSMC)(set 2) ‣ 62.1% (DSMC)(set 2)	‣ 93.7% (SSL)(set 1) ‣ 88.5% (BA/MSMC)(set 1) ‣ 93.3% (DSMC)(set 1) ‣ 93.5% (SSL)(set 2) ‣ 72.1% (BA/MSMC)(set 2) ‣ 96.7% (DSMC)(set 2)
	Zachariah 2020 [82]	Retrospective	‣ 5278 static images (3310 adenomatous and 1968 serrated polyps) ‣ WLI and NBI	‣ 634 static images	Adenomatous vs. serrated (HPs and SSLs)	n/a	94%	96%	90%	PPV 94%, and NPV 93%

AUC, area under the curve; CNN, convolutional neural network; WLI, white light image; NBI, narrow band image; FPR, false positive rate; FNR, false negative rate; PDR, polyp detection rate; ADR, adenoma detection rate; CS, colonoscopy; HP, hyperplastic polyp; AD, adenoma; CE, chromoendoscopy; NP neoplastic polyp; PPV, positive predictive value; NPV, negative predictive value; SSL, sessile serrated lesion; BA, basic adenoma; MSMC, mucosal or superficial submucosal cancer; DSMC, deep submucosal cancer

Table 3. Deep learning-based systems for capsule endoscopy.

	Reference (Year)	Classification or detection	Capsule system	CNN archi-tecture	No. of training/testing images	AUC	Accu-racy	Sensi-tivity	Speci-ficity
Developing CNN system	Zou (2015) [90]	Localization	n/a	AlexNet	6000/1500	n/a	95.5%	n/a	n/a
	Segui (2016) [92]	Scene classification	PillCam® SB2	n/a	100,000/20,000	n/a	96.0%	n/a	n/a
	Yuan (2017) [65]	Polyp	n/a	SSAEIM	4000 (abnormal 1000)/n/a	n/a	98.0%	n/a	n/a
	Zhou (2017) [66]	Celiac disease	PillCam® SB2	GoogLeNet	400 (abnormal 200) /n/a	n/a	n/a	100% per patient	100% per patient
	He (2018) [67]	Hookworm	n/a	n/a	441,624 (abnormal 4828)/11-fold cross-validation	0.895	88.5%	84.6%	88.6%
	Jia (2016) [91]	Bleeding	n/a	AlexNet	8200 (abnormal 2050)/1800 (abnormal 800)	n/a	0.9955 (F1 score)	99.2%	n/a
	Li (2017) [68]	Bleeding	n/a	LeNet AlexNet GoogLeNet VGG-Net	9672 (abnormal 312)/2418 (abnormal 78)	n/a	96.82 (F-measure) 98.72 (F-measure)99.36 (F-measure) 98.72 (F-measure)	96.20% 98.72% 98.73% 98.72%	99.91% 99.96% 100% 99.96%
	Aoki (2019) [89]	Bleeding	PillCam® SB2/SB3	ResNet	27,847 (abnormal 6503)/10,208 (abnormal 208)	0.9998	99.89%	96.63%	99.96%
	Aoki (2019) [69]	Erosion/ulcer	PillCam® SB2/SB3	SSD	5360 (abnormal 5360)/10,440 (abnormal 440)	0.958	90.8%	88.2%	90.9%
	Fan (2019) [70]	Erosion ulcer	PillCam®	AlexNet	12,910 (abnormal 5910)/8250 (abnormal 2250)	0.9863 0.9891	95.34% 95.16%	93.67% 96.80%	95.98% 94.79%
	Alaskar (2019) [71]	Ulcer*	n/a	AlexNet GoogLeNet	336 (abnormal 256)/105 (abnormal 80)	1 1	100% 100%	100% 100%	100% 100%
	Klang (2019) [72]	Ulcer	PillCam® SB3	Xception	17,640 (abnormal 7391)/5-fold cross-validation	0.99	95.4%–96.7%	92.5%–97.1%	96.0%–98.1%
	Leenhardt (2019) [67]	Angioectasia	PillCam® SB3	Semantic Segmen-tation Algorithm	600 (abnormal 300)/600 (abnormal 300)	n/a	n/a	100%	96.0%
	Tsuboi (2019) [73]	Angioectasia	PillCam® SB2/SB3	SSD	2237 (abnormal 2237)/10,448 (abnormal 488)	0.998	98.4%	98.8%	98.4%
	Iakovidis (2018) [99]	Various lesions**	MiroCam®	WCNN	852 (abnormal 423)/344 (abnormal 172)	n/a	89.2%	92.4%	85.8%
	Ding (2019)^† [92]	Various lesions***	Ankon CE system	ResNet	158,235 (abnormal n/a)/113,268,334 (abnormal n/a)	n/a	n/a	95.4%	96.99%
	Saito (2020) [93]	Various lesions****	PillCam® SB2/SB3	SSD	30584 (abnormal 30584) /17507 (abnormal 7507)	0.911	84.5%	90.7%	79.8%
	Reference (Year)	Class	Capsule system	CNN model	No. of testing videos	Read-ing time by only physicians	Reading time by physi-cians using CNN	Sensi-tivity per lesion	Sensi-tivity per video
Test using full-videos	Ding (2019)^†† [92]	Various lesions***	Ankon CE system	ResNet	5000	96.6 min	5.9 min	99.90%	99.88%
	Aoki (2019) [99]	Erosion/ulcer	PillCam® SB3	SSD	20	12.2 min (ex-pert) 207 min (trainee)	3.1 min (expert) 5.2 min (trainee)	87% (expert) 55% (trainee)	n/a

^†,††Same article.

*Esophageal and gastric ulcers

**Vascular anomalies (small bowel angioectasia, lymphangiectasias, and blood in the lumen), polypoid anomalies (lymphoid nodular hyperplasia, lymphoma, and Peutz-Jeghers polyps), and inflammatory anomalies (ulcers, aphthae, mucosal breaks with surrounding erythema, cobblestone mucosa, luminal stenoses and/or fibrotic strictures, and mucosal/villous edema).

***Clinically significant abnormal lesions (inflammation, ulcer, polyps, protruding lesion, vascular disease, bleeding, parasite, and diverticulum) and normal variants (lymphangiectasia, lymphoid hyperplasia).

Abbreviations: CNN, convolutional neural network; SSAEIM, stacked sparse autoencoder with image manifold constraint; SSD, Single Shot MultiBox Detector; WCNN, Weakly-supervised CNN; CE capsule endoscopy

Figure 3. Representative images of various abnormalities captured by capsule endoscopy.

Hirasawa T, Aoyama K, Tanimoto T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic image. Gastric Cancer. 2018;21:653–660.

 PubMed Web of Science ®Google Scholar

Ishioka M, Hirasawa T, Tada T. Detecting gastric cancer from video images using convolutional neural networks. Dig Endosc. 2019;31:e34–e35.

 PubMed Web of Science ®Google Scholar

Wu L, Zhou W, Wan X, et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy. 2019;51:522–531.

 PubMedGoogle Scholar

Kanesaka T, Lee T-C, Uedo N, et al. Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging. Gastrointest Endosc. 2018;87:1339–1344.

 PubMed Web of Science ®Google Scholar

Li L, Chen Y, Shen Z, et al. Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging. Gastric Cancer. 2020;23:126–132.

 PubMed Web of Science ®Google Scholar

Horiuchi Y, Aoyama K, Tokai Y, et al. Convolutional neural network for differentiating gastric cancer from gastritis using magnified endoscopy with narrow band imaging. Dig Dis Sci. 2020;65(5):1355-1363.

 Google Scholar

Zhu Y, Wang QC, Xu MD, et al. Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc. 2019;89:806–815.

 PubMed Web of Science ®Google Scholar

Shichijo S, Nomura S, Aoyama K, et al. Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images. EBioMedicine. 2017;25:106–111.

 PubMed Web of Science ®Google Scholar

Itoh T, Kawahira H, Nakashima H, et al. Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images. Endosc Int Open. 2018;06:E139–E144.

 Google Scholar

Nakashima H, Kawahira H, Kawachi H, et al. Artificial intelligence diagnosis of Helicobacter pylori infection using blue laser imaging-bright and linked color imaging: a single-center prospective study. Ann Gastroenterol. 2018;31:462–468.

 PubMed Web of Science ®Google Scholar

Guimarães P, Keller A, Fehlmann T, et al. Deep- learning based detection of gastric precancerous conditions. Gut. 2020;69:4–6.

 PubMed Web of Science ®Google Scholar

Takiyama H, Ozawa T, Ishihara S, et al. Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks. Sci Rep. 2018 14;8(1):7497.

 PubMedGoogle Scholar

Wu L, Zhang J, Zhou W, et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut. 2019;68:2161–2169.

 PubMed Web of Science ®Google Scholar

Urban G, Tripathi P, Alkayali T, et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology. 2018;155(4):1069–1078 e8.

 PubMed Web of Science ®Google Scholar

Misawa M, Kudo SE, Mori Y, et al. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology. 2018;154(8):2027–2029 e3.

 PubMed Web of Science ®Google Scholar

Wang P, Xiao X, Glissen Brown JR, et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng. 2018;2(10):741–748.

 PubMed Web of Science ®Google Scholar

Yamada M, Saito Y, Imaoka H, et al. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci Rep. 2019;9(1):14465.

 PubMedGoogle Scholar

Becq A, Chandnani M, Bharadwaj S, et al. Effectiveness of a deep-learning polyp detection system in prospectively collected colonoscopy videos with variable bowel preparation quality. J Clin Gastroenterol. 2020;54(6):554–557.

 Google Scholar

Klare P, Sander C, Prinzen M, et al. Automated polyp detection in the colorectum: a prospective study (with videos). Gastrointest Endosc. 2019;89(3):576–582 e1.

 PubMed Web of Science ®Google Scholar

Wang P, Berzin TM, Glissen Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019;68(10):1813–1819.

 PubMed Web of Science ®Google Scholar

Wang P, Liu X, Berzin TM, et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020;5(4):343–351.

 Web of Science ®Google Scholar

Byrne MF, Chapados N, Soudan F, et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2019;68(1):94–100.

 Google Scholar

Komeda Y, Handa H, Watanabe T, et al. Computer-aided diagnosis based on convolutional neural network system for colorectal polyp classification: preliminary experience. Oncology. 2017;93(Suppl 1):30–34.

 PubMedGoogle Scholar

Chen PJ, Lin MC, Lai MJ, et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology. 2018;154(3):568–575.

 PubMed Web of Science ®Google Scholar

Song EM, Park B, Ha CA, et al. Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model. Sci Rep. 2020;10(1):30.

 PubMed Web of Science ®Google Scholar

Zachariah R, Samarasena J, Luba D, et al. Prediction of polyp pathology using convolutional neural networks achieves “Resect and discard” thresholds. Am J Gastroenterol. 2020;115(1):138–144.

 PubMed Web of Science ®Google Scholar

Zou Y, Li L, Wang Y, et al. Classifying digestive organs in wireless capsule endoscopy images based on deep convolutional neural network. In: 2015 IEEE international conference on Digital Signal Processing (DSP),  Singapore; 2015. p. 1274–1278.

 Google Scholar

Seguí S, Drozdzal M, Pascual G, et al. Generic feature learning for wireless capsule endoscopy analysis. Comput Biol Med. 2016;79:163–172.

 PubMed Web of Science ®Google Scholar

Yuan Y, Meng MQ. Deep learning for polyp recognition in wireless capsule endoscopy images. Med Phys. 2017;44(4):1379–1389.

 PubMed Web of Science ®Google Scholar

Zhou T, Han G, Li BN, et al. Quantitative analysis of patients with celiac disease by video capsule endoscopy: A deep learning method. Comput Biol Med. 2017;85:1–6.

 PubMed Web of Science ®Google Scholar

Leenhardt R, Vasseur P, Li C, et al. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy. Gastrointest Endosc. 2019;89(1):189–194.

 PubMed Web of Science ®Google Scholar

Jia X, Meng MQ. A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images. Conference proceeding IEEE engineering in medicine and biology society, Orlando, Florida, USA; 2016. p. 639–642.

 Google Scholar

Aoki T, Yamada A, Kato Y, et al. Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network. J Gastroenterol Hepatol. 2019. DOI:10.1111/jgh.14941.

 Web of Science ®Google Scholar

Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118.

 PubMed Web of Science ®Google Scholar

Aoki T, Yamada A, Aoyama K, et al. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc. 2019;89(2):357–363.e352.

 PubMed Web of Science ®Google Scholar

Fan S, Xu L, Fan Y, et al. Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images. Phys Med Biol. 2018;63(16):165001.

 PubMed Web of Science ®Google Scholar

Alaskar H, Hussain A, Al-Aseem N, et al. Application of convolutional neural networks for automated ulcer detection in wireless capsule endoscopy images. Sensors (Basel). 2019;19(6):1265.

 PubMed Web of Science ®Google Scholar

Klang E, Barash Y, Margalit RY, et al. Deep learning algorithms for automated detection of Crohn’s disease ulcers by video capsule endoscopy. Gastrointest Endosc. 2020;91(3):606–613.e602.

 PubMed Web of Science ®Google Scholar

Tsuboi A, Oka S, Aoyama K, et al. Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images. Dig Endosc. 2020;32(3):382–390.

 Google Scholar

Aoki T, Yamada A, Aoyama K, et al. Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading. Dig Endosc. 2020;32(4):585–591.

 PubMed Web of Science ®Google Scholar

He JY, Wu X, Jiang YG, et al. Hookworm detection in wireless capsule endoscopy images with deep learning. IEEE Trans Image Process. 2018;27(5):2379–2392.

 PubMed Web of Science ®Google Scholar