IMPORTANCE Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored. OBJECTIVETo evaluate an expert-level AI-based assistive tool when used by pathologists for the grading of prostate biopsies. DESIGN, SETTING, AND PARTICIPANTSThis diagnostic study used a fully crossed multiple-reader, multiple-case design to evaluate an AI-based assistive tool for prostate biopsy grading. Retrospective grading of prostate core needle biopsies from 2 independent medical laboratories in the US was performed between October 2019 and January 2020. A total of 20 general pathologists reviewed 240 prostate core needle biopsies from 240 patients. Each pathologist was randomized to 1 of 2 study cohorts. The 2 cohorts reviewed every case in the opposite modality (with AI assistance vs without AI assistance) to each other, with the modality switching after every 10 cases. After a minimum 4-week washout period for each batch, the pathologists reviewed the cases for a second time using the opposite modality. The pathologist-provided grade group for each biopsy was compared with the majority opinion of urologic pathology subspecialists. EXPOSURE An AI-based assistive tool for Gleason grading of prostate biopsies. MAIN OUTCOMES AND MEASURES Agreement between pathologists and subspecialists with andwithout the use of an AI-based assistive tool for the grading of all prostate biopsies and Gleason grade group 1 biopsies. RESULTSBiopsies from 240 patients (median age, 67 years; range, 39-91 years) with a median prostate-specific antigen level of 6.5 ng/mL (range, 0.6-97.0 ng/mL) were included in the analyses.Artificial intelligence-assisted review by pathologists was associated with a 5.6% increase (95% CI, 3.2%-7.9%; P < .001) in agreement with subspecialists (from 69.7% for unassisted reviews to 75.3% for assisted reviews) across all biopsies and a 6.2% increase (95% CI, 2.7%-9.8%; P = .001) in agreement with subspecialists (from 72.3% for unassisted reviews to 78.5% for assisted reviews) for grade group 1 biopsies. A secondary analysis indicated that AI assistance was also associated with improvements in tumor detection, mean review time, mean self-reported confidence, and interpathologist agreement. CONCLUSIONS AND RELEVANCEIn this study, the use of an AI-based assistive tool for the review of prostate biopsies was associated with improvements in the quality, efficiency, and consistency of cancer detection and grading.

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For loss and theft in the transport of radioactive materials by a single vehicle, this article summarizes the characteristics of “illegal movement” and establishes a security system that senses its inter-relationship and responds though the group network relationship. The security system reminds the vehicle crew through on-site response and linkage response. A failure detection method for on-site response is proposed, that is, the push model is used first, and when the measurement results are suspected, the pull model is used to further confirm the failure. The failure detection for linkage response adopts the push model. According to the different security requirements of the basic and enhanced transportation, the principle of setting the timeout threshold in the failure detection algorithm is proposed. In the enhanced type, the value is smaller, otherwise the value is larger. A specific timeout threshold quantification scheme is proposed. Experiments show that the method proposed in this article is effective.

The loss and theft of radioactive material in transport can be attributed to the illegal movement. In order to distinguish it from the movement caused by the turbulence of the transportation vehicle, this paper proposes the criterion of “illegal movement” as the movement of radioactive materials outside the transportation compartment. Since the interior of the compartment is generally a metal environment, this paper proposes wireless signal strength data as a sensing method. The wireless signal strength data is filtered and converted into distance data. We construct a spatial triangle perpendicular to the top and sides of the compartment based on the distance data. When the radioactive material is inside the compartment, the angle between its corresponding point and the top plane of the compartment is less than 90°. Once it moves out of the compartment, the angle will be greater than 90°. Based on this, a sensing method of “illegal movement” based on spatial triangles is proposed. The simulation research shows that the scheme proposed in this paper is feasible.