Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.

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Objective To demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL). Materials and Methods Deep learning models were trained at each participating institution using local clinical data, and an additional model was trained using FL across all of the institutions. Results We found that the FL model exhibited superior performance and generalizability to the models trained at single institutions, with an overall performance level that was significantly better than that of any of the institutional models alone when evaluated on held-out test sets from each institution and an outside challenge dataset. Discussion The power of FL was successfully demonstrated across 3 academic institutions while avoiding the privacy risk associated with the transfer and pooling of patient data. Conclusion Federated learning is an effective methodology that merits further study to enable accelerated development of models across institutions, enabling greater generalizability in clinical use.

The COBRA score offers a straightforward, validated risk-stratification tool that incorporates the relative contribution of tumor stage and lymph node involvement to patient prognosis after cystectomy for UCB. Cancer 2017;123:4574-4582. © 2017 American Cancer Society.

Background The Prostate Imaging Reporting and Data System (PI‐RADS) provides guidelines for risk stratification of lesions detected on multiparametric MRI (mpMRI) of the prostate but suffers from high intra/interreader variability. Purpose To develop an artificial intelligence (AI) solution for PI‐RADS classification and compare its performance with an expert radiologist using targeted biopsy results. Study Type Retrospective study including data from our institution and the publicly available ProstateX dataset. Population In all, 687 patients who underwent mpMRI of the prostate and had one or more detectable lesions (PI‐RADS score >1) according to PI‐RADSv2. Field Strength/Sequence T2‐weighted, diffusion‐weighted imaging (DWI; five evenly spaced b values between b = 0–750 s/mm2) for apparent diffusion coefficient (ADC) mapping, high b‐value DWI (b = 1500 or 2000 s/mm2), and dynamic contrast‐enhanced T1‐weighted series were obtained at 3.0T. Assessment PI‐RADS lesions were segmented by a radiologist. Bounding boxes around the T2/ADC/high‐b value segmentations were stacked and saved as JPEGs. These images were used to train a convolutional neural network (CNN). The PI‐RADS scores obtained by the CNN were compared with radiologist scores. The cancer detection rate was measured from a subset of patients who underwent biopsy. Statistical Tests Agreement between the AI and the radiologist‐driven PI‐RADS scores was assessed using a kappa score, and differences between categorical variables were assessed with a Wald test. Results For the 1034 detection lesions, the kappa score for the AI system vs. the expert radiologist was moderate, at 0.40. However, there was no significant difference in the rates of detection of clinically significant cancer for any PI‐RADS score in 86 patients undergoing targeted biopsy (P = 0.4–0.6). Data Conclusion We developed an AI system for assignment of a PI‐RADS score on segmented lesions on mpMRI with moderate agreement with an expert radiologist and a similar ability to detect clinically significant cancer. Level of Evidence 4 Technical Efficacy Stage 2

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