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.