What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

Table 2 Comparison of AI and clinical assessment methods in the diagnosis field of PCa

Comparison	AI methods		Clinical assessment methods
Comparison	HC model	DL model	Clinical assessment methods
Overall performance	Relatively high		Relatively poor
SROC-AUC^*	0.87		0.82
Pooled sensitivity^*	0.90		0.93
Pooled specificity^*	0.60		0.46
Qualitative or quantitative	Quantitative		Semi-quantitative
Expert dependence	Moderate	Low	High
Consistency	High		Low
Manual delineation	Yes	No	No
Features	High-throughput features extracted using specific algorithms (e.g., shape, histogram, and textural features)	Automatic extraction of deep and subtle image features using networks with substantial parameters	Features for visual assessments (e.g., location, shape, size, and intensity) and some clinical characteristics

AI artificial intelligence, csPCa clinically significant prostate cancer, DL deep learning, HC hand-crafted, PCa prostate cancer, PI-RADS prostate imaging reporting and data system, SROC-AUC area under the summary receiver operating characteristic curves
^*Performance indexes pooled across the studies on csPCa diagnoses

ISSN: 2054-9369