The synergy between AI and radiologist in advancing digital mammography: comparative study between stand-alone radiologist and concurrent use of artificial intelligence in BIRADS 4 and 5 female patients

Abstract

Abstract Background Recent significant advancements in speed and machine learning have profoundly changed artificial intelligence (AI). In order to evaluate the value of AI in the detection and diagnosis of BIRADS 4 and 5 breast lesions visible on digital mammography pictures, we compared it to a radiologist. The gold standard was tissue core biopsy and pathology. A total of 130 individuals with 134 BIRADS 4 or 5 mammography lesions were included in the study, and all relevant digital mammography pictures were exported to an AI software system. Objectives The goal of this investigation was to determine how well artificial intelligence performs in digital mammography when compared to a radiologist in identifying and diagnosing BIRADS 4 and 5 breast lesions. Methods A total of 134 BIRADS 4 and 5 breast lesions in 130 female patients were discovered using digital mammography on both the craniocaudal and mediolateral oblique planes. All mammograms were transferred to an AI software system for analysis, and the results were compared in accordance with the histopathological results, which served as standard of reference in all lesions. Results Artificial intelligence was found to be more accurate (90.30%) than radiologist (82.84%) and shows higher positive predictive value (94.5%) than radiologist (82.8%) regarding suspecting malignancy in digital mammography with BIRADS 4 and 5 lesions, while the radiologist achieved higher sensitivity (100%) than AI (93.7%) in detecting malignancy in BIRADS 4 and 5 lesions. Conclusions Radiologist was found to be more sensitive than AI in detecting malignancy in BIRADS 4 and 5 lesions but AI had a higher positive predictive value. However, specificity as well as negative predictive value could not be assessed for the radiologist, hence could not be compared with AI values because the inclusion criteria of the study did not include BIRADS 1, 2 and 3 so benign-looking lesions by digital mammography were not involved to measure specificity and negative predictive values. All in all, based on the available data, AI was found to be more accurate than radiologist regarding suspecting malignancy in digital mammography. AI can run hand in hand with human experience to give best health-care service in screening and/or diagnosing patients with breast cancer.