Accuracy of machine learning models using ultrasound images in prostate cancer diagnosis: a systematic review

BACKGROUND In prostate cancer (PCa) diagnosis, many developed machine learning (ML) models using ultrasound images show good accuracy. This study aimed to analyze the accuracy of neural network ML models in PCa diagnosis using ultrasound images. METHODS The protocol was registered with PROSPERO registration number CRD42021277309. Three reviewers independently conducted a literature search in 5 online databases (PubMed, EBSCO, Proquest, ScienceDirect, and Scopus). We included all cohort, case-control, and cross-sectional studies in English, that used neural networks ML models for PCa diagnosis in humans. Conference/review articles and studies with combination examination with magnetic resonance imaging or had no diagnostic parameters were excluded. RESULTS Of 391 titles and abstracts screened, 9 articles relevant to the study were included. Risk of bias analysis was conducted using the QUADAS-2 tool. Of the 9 articles, 5 used artificial neural networks, 1 used deep learning, 1 used recurrent neural networks, and 2 used convolutional neural networks. The included articles showed a varied area under the curve (AUC) of 0.76–0.98. Factors affecting the accuracy of artificial intelligence (AI) were the AI model, mode and type of transrectal sonography, Gleason grading, and prostate-specific antigen level. CONCLUSIONS The accuracy of neural network ML models in PCa diagnosis using ultrasound images was relatively high, with an AUC value above 0.7. Thus, this modality is promising for PCa diagnosis that can provide instant information for further workup and help doctors decide whether to perform a prostate biopsy.