Role of artificial intelligence in the diagnosis of oesophageal neoplasia: 2020 an endoscopic odyssey

Table 1 Studies showing accuracy in the detection of Barrett’s oesophagus dysplasia for each endoscopic modality

	I-scan optical enhancement	NBI	BLI
Ref.	Everson et al[11]	Sharma et al[12]	Subramaniam et al[13]
Features assessed	Mucosal pit pattern, vessels	Mucosal pit pattern, vessels	Colour, mucosal pit patterns, vessels
Accuracy	Experts = 84%, non-experts = 76%	85%	Experts = 95.2%, non-experts = 88.3%
Sensitivity	Experts = 77%, non-experts = 81%	80%	Experts = 96%, non-experts = 95.7%
Specificity	Experts = 92%, non-experts = 70%	88%	Experts = 94.4%, non-experts = 80.8%

NBI: Narrow-band imaging; BLI: Blue laser imaging.

Table 2 Summary of all the studies investigating the development of machine learning algorithms for the detection of dysplasia in Barrett’s oesophagus

Ref.	Year	Endoscopic processor	Study design	Study aim	Algorithm used	No. of patients	No. of BE images	Sensitivity	Specificity
Van der Sommen et al[21]	2016	WLE Fujinon	Retrospective	Assess feasibility of computer system to detect early neoplasia in BE	Machine learning, specific textures and colour filters	44	100 (60 dysplasia, 40 NDBE)	83% (per image), 86% (per patient)	83% (per image), 87% (per patient)
Sweger et al[28]	2017	VLE	Retrospective	Assess feasibility of computer algorithm to identify BE dysplasia on ex vivo VLE images	Several machine learning methods; discriminant analysis, support vector machine, AdaBoost, random forest, K-nearest neighbors	19	60 (30 dysplasia, 30 NDBE)	90%	93%
Ebigbo et al[29]	2018	WLE, NBI, Olympus	Retrospective	Detection of early oesophageal cancer	Deep CNN with a residual net architecture	50 with early neoplasia	248	97% (WLE), 94% (NBI)	88% (WLE), 80% (NBI)
de Groof et al[30]	2019	WLE, Fujinon	Prospective	Develop CAD to detect early neoplasia in BE	Supervised Machine learning. Trained on colour and texture features	60	60 (40 dysplasia, 20 NDBE)	95%	85%
de Groof et al[22]	2020	WLE Fujinon, WLE Olympus	Retrospective, Prospective	Develop and validate deep learning CAD to improve detection of early neoplasia in BE	CNN pretrained on GastroNet. Hybrid ResNet/U-Net model	669	1704 (899 dysplasia, 805 NDBE)	90%	88%
Hashimoto et al[31]	2020	WLE, Olympus	Retrospective	Assess if CNN can aid in detecting early neoplasia in BE	CNN pretrained on image net and based on Xception architecture and YOLO v2	100	1832 (916 dysplasia, 916 NDBE)	96.4%	94.2%
de Groof et al[23]	2020	WLE, Fujinon	Prospective	Evaluate CAD assessment of early neoplasia during live endoscopy	CNN pretrained on GastroNet; hybrid ResNet/U-Net Model	20	-	91%	89%
Struyvenberg MR et al[27]	2020	VLE	Prospective	Evaluate feasibility of automatic data extraction followed by CAD using mutiframe approach to detect to dysplasia in BE	CAD multiframe analysis with principal component analysis	29	-	-	-

BE: Barrett’s oesophagus; WLE: White light endoscopy; NBI: Narrow band imaging; VLE: Volumetric laser endomicroscopy; CNN: Convolutional neural network; CAD: Computer-aided detection.

Table 3 Summary of all the studies investigating the development of machine learning algorithms for the detection of early squamous cell neoplasia

Ref.	Year	Endoscopic processor	Study design	Study aim	Algorithm used	No. of patients	No. of images	Sensitivity	Specificity
Shin et al[37]	2015	High resolution micro-endoscopy	Retrospective	Differentiate neoplastic and non-neoplastic squamous oesophageal mucosa	Quantitative image analysis. Two-class linear discriminant analysis to develop classifier	177	375	87%	97%
Quang et al[38]	2016	High resolution micro-endoscopy	Retrospective	Differentiate neoplastic and non-neoplastic squamous oesophageal mucosa	Two-class linear discriminant analysis to develop classifier	3	-	95%	91%
Horie et al[39]	2018	WLE, NBI, Olympus	Retrospective	Ability of AI to detect oesophageal cancer	Deep CNN (Multibox detector architecture)	481	-	97%	-
Everson et al[16]	2019	Magnified NBI, Olympus	Retrospective	Develop AI system to classify IPCL patterns as normal/abnormal in endoscopically resectable lesions real time	CNN, explicit class activation maps generated to depict area of interest for CNN	17	7046	89%	98%
Nakagawa et al[34]	2019	Magnified and non-magnified, NBI, BLI, Olympus, Fujifilm	Retrospective	Predict depth of invasion of ESCN	Deep CNN (multibox detector architecture)	959	15,252	90.1%	95.8%
Kumagai et al[36]	2019	ECS	Retrospective	Deep learning AI to analyse ECS images as possible replacement of biopsy-based histology	CNN constructed based on GoogLeNet	-	6235	92.6%	89.3%
Zhao et al[40]	2019	ME NBI, Olympus	Retrospective	Classification of IPCLs to improve ESCN detection	A double-labeling fully convolutional network	219	-	87%	84.1%
Guo et al[3]	2020	ME and non-ME NBI, olympus	Retrospective	Develop a CAD for real-time diagnosis of ESCN	Model based on SegNet architecture	2672	13144 images (4250 malignant, 8894 non-cancerous), 168865 video frames	Images = 98.04%, non-magnified video = 60.8%, magnified video = 96.1%	Images = 95.03%, non-magnified /magnified video = 99.9%
Tokai et al[41]	2020	WLE, NBI, Olympus	Retrospective	Ability of AI to measure squamous cell cancer depth	Deep CNN	-	2044	84.1%	73.3%
Ohmori et al[42]	2020	Magnified and non-magnified, WLE, NBI, BLI, Olympus, Fujifilm	Retrospective	Detect Oesophageal squamous cell cancer	CNN	-	11806 non- magnified images, 11483 magnified images	Non-ME WLE = 90%, non-ME NBI/BLI = 100%, ME = 98%	Non-ME WLE = 76%, non-ME NBI/BLI = 63%, ME = 56%

WLE: White light endoscopy; NBI: Narrow band imaging; AI: Artificial intelligence; CNN: Convolutional neural network; IPCL: Intrapapillary capillary loops; BLI: Blue laser imaging; ESCN: Early squamous cell neoplasia; ECS: Endocytoscopic system; ME: Magnification endoscopy.