Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides

Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. Reorganization and classification of complex histologic patterns of RCC on biopsy and surgical resection slides under a microscope remains a heavily specialized, error-prone, and time-consuming task for pathologists. In this study, we developed a deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC, chromophobe RCC, renal oncocytoma, and normal. In addition to the whole-slide classification pipeline, we visualized the identified indicative regions and features on slides for classification by reprocessing patch-level classification results to ensure the explainability of our diagnostic model. We evaluated our model on independent test sets of 78 surgical resection whole slides and 79 biopsy slides from our tertiary medical institution, and 917 surgical resection slides from The Cancer Genome Atlas (TCGA) database. The average area under the curve (AUC) of our classifier on the internal resection slides, internal biopsy slides, and external TCGA slides is 0.98 (95% confidence interval (CI): 0.97–1.00), 0.98 (95% CI: 0.96–1.00) and 0.97 (95% CI: 0.96–0.98), respectively. Our results suggest that the high generalizability of our approach across different data sources and specimen types. More importantly, our model has the potential to assist pathologists by (1) automatically pre-screening slides to reduce false-negative cases, (2) highlighting regions of importance on digitized slides to accelerate diagnosis, and (3) providing objective and accurate diagnosis as the second opinion

Evaluation of an Artificial Intelligence-Augmented Digital System for Histologic Classification of Colorectal Polyps

Importance: Colorectal polyps are common, and their histopathologic classification is used in the planning of follow-up surveillance. Substantial variation has been observed in pathologists\u27 classification of colorectal polyps, and improved assessment by pathologists may be associated with reduced subsequent underuse and overuse of colonoscopy. Objective: To compare standard microscopic assessment with an artificial intelligence (AI)-augmented digital system that annotates regions of interest within digitized polyp tissue and predicts polyp type using a deep learning model to assist pathologists in colorectal polyp classification. Design, Setting, and Participants: In this diagnostic study conducted at a tertiary academic medical center and a community hospital in New Hampshire, 100 slides with colorectal polyp samples were read by 15 pathologists using a microscope and an AI-augmented digital system, with a washout period of at least 12 weeks between use of each modality. The study was conducted from February 10 to July 10, 2020. Main Outcomes and Measures: Accuracy and time of evaluation were used to compare pathologists\u27 performance when a microscope was used with their performance when the AI-augmented digital system was used. Outcomes were compared using paired t tests and mixed-effects models. Results: In assessments of 100 slides with colorectal polyp specimens, use of the AI-augmented digital system significantly improved pathologists\u27 classification accuracy compared with microscopic assessment from 73.9% (95% CI, 71.7%-76.2%) to 80.8% (95% CI, 78.8%-82.8%) (P \u3c.001). The overall difference in the evaluation time per slide between the digital system (mean, 21.7 seconds; 95% CI, 20.8-22.7 seconds) and microscopic examination (mean, 13.0 seconds; 95% CI, 12.4-13.5 seconds) was -8.8 seconds (95% CI, -9.8 to -7.7 seconds), but this difference decreased as pathologists became more familiar and experienced with the digital system; the difference between the time of evaluation on the last set of 20 slides for all pathologists when using the microscope and the digital system was 4.8 seconds (95% CI, 3.0-6.5 seconds). Conclusions and Relevance: In this diagnostic study, an AI-augmented digital system significantly improved the accuracy of pathologic interpretation of colorectal polyps compared with microscopic assessment. If applied broadly to clinical practice, this tool may be associated with decreases in subsequent overuse and underuse of colonoscopy and thus with improved patient outcomes and reduced health care costs.

Breeze, ein spielerisches Biofeedback Atemtraining für das Smartphone: Physiologische Reaktionen und subjektive Einschätzungen aus einem Labor- und Online-Experiment

Hintergrund: Langsames Atmen hat eine positive Wirkung auf die Herzfunktion und auf das psychische Wohlbefinden. Daher werden entsprechende Atemübungen oft bei chronischen Krankheiten empfohlen; sie werden allerdings aus verschiedenen Gründen nur von bestimmten Personengruppen ausgeübt und haben somit eine eingeschränkte Reichweite und Wirkung. Ziel: Die Breeze App verfolgt das Ziel, die Reichweite von Atemübungen mit einem spielerischen und skalierbaren Biofeedback-Ansatz zu erhöhen. Methode: Grundlage der Atemübung Breeze ist die Erkennung der Atmung mit dem Mikrofon des Smartphones, um damit beim Ausatmen «Rückenwind» für ein virtuelles Segelboot zu erzeugen und es somit zu beschleunigen. Entspricht der Atmungs-Zyklus einem validierten Muster (z.B. 4s Einatmung, 2s Ausatmung und 4s Pause), kann mit dem Segelboot, welches in Echtzeit auf dem Bildschirm des Smartphones dargestellt wird, die grösste Reisedistanz zurückgelegt werden. Es wurden Labor- und Online-Experimente durchgeführt, um Breeze hinsichtlich physiologischer Effekte und subjektiver Einschätzungen bei erwachsenen Personen zu evaluieren. Ergebnisse: Im Labor (N=16) konnte gezeigt werden, dass Breeze nicht nur zu einer Steigerung der Herzfrequenzvariabilität geführt hat (p<.001), sondern auch gegenüber einer validierten Atemübung ohne spielerischen Ansatz von 14 (87.5%) Personen präferiert wurde. Ein Online-Experiment mit Teilnehmenden, welche im Schnitt nur wenig bis gar keine Erfahrung mit Atemübungen hatten, zeigte darüber hinaus, dass die wahrgenommene Entspannung durch Breeze (N=88) mit der einer validierten Atemübung (N=82) vergleichbar ist und 51 (58.0%) Personen Breeze im Alltag nutzen würden. Zusammenfassung: Breeze hat mit seinem spielerischen Ansatz das Potential, die Reichweite von Atemübungen zu erhöhen, was insbesondere für das Selbstmanagement bei chronischen Krankheiten relevant sein kann