Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma.

Computer-aided diagnosis using deep learning (CAD-DL) may be an instrument to improve endoscopic assessment of Barrett’s oesophagus (BE) and early oesophageal adenocarcinoma (EAC). Based on still images from two databases, the diagnosis of EAC by CAD-DL reached sensitivities/specificities of 97%/88% (Augsburg data) and 92%/100% (Medical Image Computing and Computer-Assisted Intervention [MICCAI] data) for white light (WL) images and 94%/80% for narrow band images (NBI) (Augsburg data), respectively. Tumour margins delineated by experts into images were detected satisfactorily with a Dice coefficient ( D ) of 0.72. This could be a first step towards CAD-DL for BE assessment. If developed further, it could become a useful adjunctive tool for patient management.The incidence of BE and EAC in the West is rising significantly, and because of its close association with the metabolic syndrome this trend is expected to continue.1–3 Reports of CAD in BE analysis have used mainly handcrafted features based on texture and colour.4–7 In our study, two databases (Augsburg data and the ‘Medical Image Computing and Computer Assisted-Intervention’ [MICCAI] data) were used to train and test a CAD system on the basis of a deep convolutional neural net (CNN) with a residual net (ResNet) architecture.8 Images included 148 high-definition (1350×1080 pixels) WL and NBI of 33 early EAC and 41 areas of non-neoplastic Barrett’s mucosa in the Augsburg data set, while the MICCAI data set contained 100 high-definition (1600×1200 pixels) WL images—17 early EAC and 22 areas of non-neoplastic Barrett’s mucosa. All images were pathologically validated and this served as the ground truth for the classification task. Manual delineation of tumour margins by experts was the reference standard for the segmentation task.The ResNet …