An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis

The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoE-EREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level

Conventional transarterial chemoembolization versus drug-eluting bead transarterial chemoembolization for the treatment of hepatocellular carcinoma

BACKGROUND: To compare the overall survival of patients with hepatocellular carcinoma (HCC) who were treated with lipiodol-based conventional transarterial chemoembolization (cTACE) with that of patients treated with drug-eluting bead transarterial chemoembolization (DEB-TACE). METHODS: By an electronic search of our radiology information system, we identified 674 patients that received TACE between November 2002 and July 2013. A total of 520 patients received cTACE, and 154 received DEB-TACE. In total, 424 patients were excluded for the following reasons: tumor type other than HCC (n = 91), liver transplantation after TACE (n = 119), lack of histological grading (n = 58), incomplete laboratory values (n = 15), other reasons (e.g., previous systemic chemotherapy) (n =  114), or were lost to follow-up (n = 27). Therefore, 250 patients were finally included for comparative analysis (n = 174 cTACE; n = 76 DEB-TACE). RESULTS: There were no significant differences between the two groups regarding sex, overall status (Barcelona Clinic Liver Cancer classification), liver function (Child-Pugh), portal invasion, tumor load, or tumor grading (all p >  0.05). The mean number of treatment sessions was 4 ± 3.1 in the cTACE group versus 2.9 ± 1.8 in the DEB-TACE group (p = 0.01). Median survival was 409 days (95 % CI: 321–488 days) in the cTACE group, compared with 369 days (95 % CI: 310–589 days) in the DEB-TACE group (p = 0.76). In the subgroup of Child A patients, the survival was 602 days (484–792 days) for cTACE versus 627 days (364–788 days) for DEB-TACE (p = 0.39). In Child B/C patients, the survival was considerably lower: 223 days (165–315 days) for cTACE versus 226 days (114–335 days) for DEB-TACE (p = 0.53). CONCLUSION: The present study showed no significant difference in overall survival between cTACE and DEB-TACE in patients with HCC. However, the significantly lower number of treatments needed in the DEB-TACE group makes it a more appealing treatment option than cTACE for appropriately selected patients with unresectable HCC

Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm

In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training

Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: a pilot study

The accurate differentiation between T1a and T1b Barrett's-related cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an artificial intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett's cancer on white-light images. Endoscopic images from three tertiary care centers in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) were evaluated using the AI system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett's cancer. The sensitivity, specificity, F1 score, and accuracy of the AI system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.74, and 0.71, respectively. There was no statistically significant difference between the performance of the AI system and that of experts, who showed sensitivity, specificity, F1, and accuracy of 0.63, 0.78, 0.67, and 0.70, respectively. This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett's cancer. AI scored equally to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and real-life settings. Nevertheless, the correct prediction of submucosal invasion in Barrett's cancer remains challenging for both experts and AI

Visual impairment and blindness in institutionalized elderly in Germany

PurposeTo determine the prevalence of and identify factors associated with visual impairment and blindness in institutionalized elderly in Germany.MethodsIn this prospective multicenter cross-sectional study, ophthalmic health care need and provision were investigated in institutionalized elderly in 32 nursing homes in Germany. All participants underwent a standardized examination including medical and ocular history, refraction, visual acuity testing, tonometry, biomicroscopy, and dilated funduscopy. A standardized questionnaire was used to identify factors associated with eye healthcare utilization, visual impairment and/or blindness.ResultsVisual acuity of 566 (94.3%; 413 women and 153 men) of a total of 600 institutionalized elderly was determined. Mean age of the included patients was 82.9years (9.8). Of all participants, 30 (5.3%; 95% CI 3.4-7.2%) were blind and 106 (18.7%; 95% CI 15.5-21.9%) were moderately or severely visually impaired according to the World Health Organization definition. The 136 blind and moderately or severely visually impaired participants were older (OR, Odds Ratio=1.1, 95% CI 1.0-1.1; p<0.001), and more likely to have reduced mobility (OR=12.6, 95% CI 2.8-57.6; p=0.001).Conclusion p id=Par4 A high proportion of blindness and visual impairment was found amongst nursing home residents. Age and reduced mobility were factors associated with an increased likelihood of blindness and visual impairment. Any surveys of blindness and visual impairment excluding nursing homes may considerably underestimate the prevalence of visual impairment and blindness