A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate

Abstract

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Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review

“…Recent studies on CAD prototypes from, e.g., Winkel et al indicate the future direction of commercial CAD applications, in which AI solutions for classification and detection of PCa lesions are gaining interest. The same authors have performed a new study on CAD implementation, which has been published after the inclusion period of studies within this review, underlining the rapid development within this field [104]. It is expected that future work on PCa CAD applications for lesion classification and detection will continue, with initiatives to centralize and combine data from multiple institutions to increase generalizability and robustness of PCa CAD arising [105].…”

Fast Magnetic Resonance Imaging as a Viable Method for Directing the Prostate Cancer Diagnostic Pathway

“…This approach, however, needs further clinical testing and validation. Artificial intelligence methods that are trained on fast MRI sequences have recently shown good discriminatory power and an improvement in reader variability, and may be helpful in promoting the fast MRI approach [12]. Definitive investigations of fast MRI for directing the prostate cancer diagnostic pathway in prospective, multicentre, multiobserver settings would be welcomed.…”

A European Model for an Organised Risk-stratified Early Detection Programme for Prostate Cancer

“…Artificial intelligence methods that are trained with fast biparametric MRI have recently shown good discriminatory power and improved reader variability. They thus may be additionally helpful in enabling a "smarter" and faster MRI approach [41]. Definitive investigations of fast MRI for directing the PCa diagnosis pathway in prospective, multicentre, multiobserver settings are needed before large-scale implementation can be considered.…”