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

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.