NYUAD Assistant Professor Emerging Scholar of Computer Engineering Farah Shamout and colleagues at NYU element their findings in “Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams,” a paper printed within the journal Nature Communications.
The researchers utilized a dataset of greater than 280,000 ultrasound exams from over 140,000 sufferers examined at NYU Langone Health between 2012 and 2019 to develop and check their new system. The system is designed to establish malignant lesions in breast ultrasound pictures, with the first purpose of decreasing the frequency of false optimistic findings. It can detect most cancers by assigning a likelihood for malignancy and highlighting components of ultrasound pictures which might be related to its predictions.
To perceive the potential worth of this novel AI system in a medical setting, the researchers carried out a reader examine to match its diagnostic accuracy with ten board-certified breast radiologists. It achieved increased accuracy than the ten radiologists on common, however a hybrid mannequin that aggregated the predictions of the AI system and radiologists achieved one of the best leads to precisely detecting most cancers in sufferers. The efficiency of the AI system remained strong throughout sufferers from completely different age teams and mammographic breast densities.
“Our findings highlight the potential of AI to improve the accuracy, consistency, and efficiency of breast ultrasound diagnosis,” stated Dr. Shamout. “Importantly, AI is not a replacement for the expertise of clinicians. However, the powerful, complimentary role that AI systems can play as a decision support tool leads us to believe that they should and will be increasingly translated into clinical practice.”