Wong CF
An AI-assisted tool for efficient prostate cancer diagnosis in low-grade and low-volume cases
Pathologists diagnose prostate cancer by core needle biopsy. In low-grade and low-volume cases, they look for a few malignant glands out of hundreds within a core. They may miss a few malignant glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed a multi-resolution deep-learning pipeline to assist pathologists in detecting malignant glands in core needle biopsies of low-grade and low-volume cases. Analyzing a gland at multiple resolutions, our model exploited morphology and neighborhood information, which were crucial in prostate gland classification. We developed and tested our pipeline on the slides of a local cohort of 99 patients in Singapore. Besides, we made the images publicly available, becoming the first digital histopathology dataset of patients of Asian ancestry with prostatic carcinoma. Our multi-resolution classification model achieved an area under the receiver operating characteristic curve (AUROC) value of 0.992 (95% confidence interval [CI]: 0.985–0.997) in the external validation study, showing the generalizability of our multi-resolution approach.
ReadA Review of Apps for Programming: programming languages and making apps with apps
With the latest technologies enabling smartphones to work like mini computers, there are now numerous opportunities to use smartphones for the development of mobile apps. Given the multiple Operating Systems (OS) available, there is a significant demand for apps for the various platforms.
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