Diagnostic utility of CT angiography compared with endoscopy in patients with acute GI hemorrhage.

BACKGROUND AND AIMS: Because it is minimally invasive, CT angiography (CTA) has emerged as an attractive diagnostic tool for investigation of acute GI hemorrhage. METHODS: This study examined patients with acute GI bleeding who underwent CTA. RESULTS: CTA was the initial diagnostic examination in 177 patients, identifying upper and lower GI bleeding lesions in 16 and 27 patients, respectively. In 103 patients with an initial negative CTA, 78 had endoscopy (32 EGD and 46 colonoscopy/flexible sigmoidoscopy), of whom 52 (67%) had a bleeding lesion identified, including 23 with a high-risk bleeding lesion requiring therapy. Peptic ulcer disease and diverticular bleeding were the most commonly identified bleeding lesions. With endoscopy as a criterion standard, the sensitivity of CTA for the detection of a source of GI bleeding was 20%. CONCLUSIONS: CTA has very poor sensitivity for identification of a GI bleeding source or lesion, suggesting that CTA should not be used as an initial diagnostic test.

Automated Identification of Patent Ductus Arteriosus Using a Computer Vision Model.

OBJECTIVES: Patent ductus arteriosus (PDA) is a vascular defect common in preterm infants and often requires treatment to avoid associated long-term morbidities. Echocardiography is the primary tool used to diagnose and monitor PDA. We trained a deep learning model to identify PDA presence in relevant echocardiographic images. METHODS: Echocardiography video clips (n = 2527) in preterm infants were reviewed by a pediatric cardiologist and those relevant to PDA diagnosis were selected and labeled (PDA present/absent/indeterminate). We trained a convolutional neural network to classify each echocardiography frame of a clip as belonging to clips with or without PDA. A novel attention mechanism that aggregated predictions for all frames in each clip to obtain a clip-level prediction by weighting relevant frames. RESULTS: In early model iterations, we discovered training with color Doppler echocardiography clips produced the best performing classifier. For model training and validation, 1145 such clips from 66 patients (661 PDA+ clips, 484 PDA- clips) were used. Our best classifier for clip level performance obtained sensitivity of 0.80 (0.83-0.90), specificity of 0.77 (0.62-0.92) and AUC of 0.86 (0.83-0.90). Study level performance obtained sensitivity of 0.83 (0.72-0.94), specificity of 0.89 (0.79-1.0) and AUC of 0.93 (0.89-0.98). CONCLUSIONS: Our novel deep learning model demonstrated strong performance in classifying echocardiography clips with and without PDA. Further model development and external validation are warranted. Ultimately, integration of such a classifier into auto detection software could streamline PDA imaging workflow. This work is the first step toward semi-automated, bedside detection of PDA in preterm infants.

The role of artificial intelligence in hepatology research and practice.

PURPOSE OF REVIEW: The use of artificial intelligence (AI) in examining large data sets has recently gained considerable attention to evaluate disease epidemiology, management approaches, and disease outcomes. The purpose of this review is to summarize the current role of AI in contemporary hepatology practice. RECENT FINDINGS: AI was found to be diagnostically valuable in the evaluation of liver fibrosis, detection of cirrhosis, differentiation between compensated and decompensated cirrhosis, evaluation of portal hypertension, detection and differentiation of particular liver masses, preoperative evaluation of hepatocellular carcinoma as well as response to treatment and estimation of graft survival in patients undergoing liver transplantation. AI additionally holds great promise in examination of structured electronic health records data as well as in examination of clinical text (using various natural language processing approaches). Despite its contributions, AI has several limitations, including the quality of existing data, small cohorts with possible sampling bias and the lack of well validated easily reproducible models. SUMMARY: AI and deep learning models have extensive applicability in assessing liver disease. However, multicenter randomized controlled trials are indispensable to validate their utility.