In an exciting breakthrough, a recent study suggests that Artificial Intelligence (AI) can aid in breast cancer screening that’s as precise as the traditional method where mammograms are read by two breast radiologists. But here's the cherry on top: It might significantly reduce the workload on radiologists.
A study from the Swedish Mammography Screening withArtificial Intelligence (MASAI) trial published in Lancet Oncology unveiled that AI-supported screenings not only slashed the radiologist workload by a staggering 44% but also amped up the cancer detection rate by 20% compared to the conventional double mammography readings. AI-supported screenings not only seem safe but are potentially as precise as the traditional double reading of mammograms by two radiologists.
Over 80,000 women, with an average age of 54, took part in the MASAI trial. They were evenly divided into two groups: One benefited from AI-backed screening, while the other relied on the traditional double reading without AI intervention.
The AI system's core was to triage mammograms using malignancy risk scores, effectively determining the level of review required. The AI’s brilliance was reflected in its ability to provide risk scores between 1 to 10. Those with a risk score of 10, representing a sharp increase in cancer prevalence, were directed to double reading. Meanwhile, scores from 1 to 9 resulted in a single radiologist examination.
The results were astounding. Among the AI-supported screening participants, 244 cancers were detected among roughly 40,000 women, out of which 184 were invasive cancers. In contrast, the standard screening cohort had 203 detections in an almost similar participant pool, with 165 invasive cancers.
The detection rates portrayed a clear edge for the AI-support group with 6.1 per 1000 participants against 5.1 per 1000 for the standard screening. Both methods had a consistent false positive rate at 1.5%. However, the likelihood of a recall leading to a cancer diagnosis was higher with AI at 28.3% versus 24.8% in standard screening.
What was even more impressive is the efficiency. With an assumption of a radiologist reading 50 mammograms per hour, AI could save up to4.6 months when reading over 46,000 screenings!
Though the initial results appear promising, the path to incorporating AI in mammography screening isn’t free from hurdles. While the early results are hopeful, more comprehensive insights into patient outcomes, the potential to detect often-missed interval cancers, and the technology's cost-effectiveness are vital.
One potential concern raised by the study is the increased detection of in situ cancers with AI-backed screening (25% vs. 19% in traditional screening). These low-grade cancers might be prone to overtreatment, indicating a risk of overdiagnosis.
The future of the study looks to delve deeper into these areas. The MASAI team aims to scrutinize the biological features of detected lesions, focusing on AI-supported screening's overdiagnosis risk. AI might provide a solution to the pressing issue of radiologist workload in many breast screening programs.
While the horizon of AI-backed breast cancer screening seems bright, it’s essential to tread with caution, keeping patient safety and accuracy paramount.
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