Accurate diagnosis of endoscopic mucosal healing in ulcerative colitis using deep learning and machine learning.

BACKGROUND: In clinical applications, mucosal healing is a therapeutic goal in patients with ulcerative colitis (UC). Endoscopic remission is associated with lower rates of colectomy, relapse, hospitalization, and colorectal cancer. Differentiation of mucosal inflammatory status depends on the experience and subjective judgments of clinical physicians. We developed a computer-aided diagnostic system using deep learning and machine learning (DLML-CAD) to accurately diagnose mucosal healing in UC patients. METHODS: We selected 856 endoscopic colon images from 54 UC patients (643 images with endoscopic score 0-1 and 213 with score 2-3) from the endoscopic image database at Tri-Service General Hospital, Taiwan. Endoscopic grading using the Mayo endoscopic subscore (MES 0-3) was performed by two reviewers. A pretrained neural network extracted image features, which were used to train three different classifiers-deep neural network (DNN), support vector machine (SVM), and k-nearest neighbor (k-NN) network. RESULTS: DNN classified MES 0 to 1, representing mucosal healing, vs MES 2 to 3 images with 93.8% accuracy (sensitivity 84.6%, specificity 96.9%); SVM had 94.1% accuracy (sensitivity 89.2%, specificity 95.8%); and k-NN had 93.4% accuracy (sensitivity 86.2%, specificity 95.8%). Combined, ensemble learning achieved 94.5% accuracy (sensitivity 89.2%, specificity 96.3%). The system further differentiated between MES 0, representing complete mucosal healing, and MES 1 images with 89.1% accuracy (sensitivity 82.3%, specificity 92.2%). CONCLUSION: Our DLML-CAD diagnosis achieved 94.5% accuracy for endoscopic mucosal healing and 89.0% accuracy for complete mucosal healing. This system can provide clinical physicians with an accurate auxiliary diagnosis in treating UC.

Automated extraction of left atrial volumes from two-dimensional computer tomography images using a deep learning technique.

BACKGROUND: Precise segmentation of the left atrium (LA) in computed tomography (CT) images constitutes a crucial preparatory step for catheter ablation in atrial fibrillation (AF). We aim to apply deep convolutional neural networks (DCNNs) to automate the LA detection/segmentation procedure and create three-dimensional (3D) geometries. METHODS: Five hundred eighteen patients who underwent procedures for circumferential isolation of four pulmonary veins were enrolled. Cardiac CT images (from 97 patients) were used to construct the LA detection and segmentation models. These images were reviewed by the cardiologists such that images containing the LA were identified/segmented as the ground truth for model training. Two DCNNs which incorporated transfer learning with the architectures of ResNet50/U-Net were trained for image-based LA classification/segmentation. The LA geometry created by the deep learning model was correlated to the outcomes of AF ablation. RESULTS: The LA detection model achieved an overall 99.0% prediction accuracy, as well as a sensitivity of 99.3% and a specificity of 98.7%. Moreover, the LA segmentation model achieved an intersection over union of 91.42%. The estimated mean LA volume of all the 518 patients studied herein with the deep learning model was 123.3 ± 40.4 ml. The greatest area under the curve with a LA volume of 139 ml yielded a positive predictive value of 85.5% without detectable AF episodes over a period of one year following ablation. CONCLUSIONS: The deep learning provides an efficient and accurate way for automatic contouring and LA volume calculation based on the construction of the 3D LA geometry.