New research published in Radiology finds that ScreenPoint Medical’s Transpara Detection helps breast radiologists improve their breast cancer detection when reading mammograms and guides them to the highest suspicion areas within each study while not increasing their reading workload. Highlights from the study include:
- In 150 screening mammograms read by 12 radiologists, compared with unaided reading, radiologists using Transpara achieved higher mean areas under the receiver operating characteristic curves (0.93 vs 0.97; P < .001), with comparable sensitivity (81.7% vs 87.2%; P = .06) and specificity (89.0% vs 91.1%; P = .46).
- When adding Transpara, radiologists’ reading time remained unchanged (29.4 vs 30.8 seconds; P = .33) as well as time to first fixation (3.4 vs 3.8 seconds; P = .13), but they spent significantly more time focusing on lesions (4.4 vs 5.4 seconds; P = .006) while fixating less on the rest of the breast (breast fixation coverage, 11.1% vs 9.5%; P = .004).
The study included the most recent 2.1.0 model of ScreenPoint Medical’s Transpara Detection algorithm and concluded that “radiologists improved their breast cancer detection accuracy when reading mammography with [Transpara], spending more fixation time on suspicious areas and less on the rest of the breast, indicating a more efficient search.” This study adds to the growing evidence that Transpara AI has the potential to boost the diagnostic accuracy of radiologists, while making the most efficient use of their clinical expertise.
For more on the study:
Read more from across the web:
- AI helps radiologists focus on lesions more than entire breast on mammo – via AuntMinnie
- AI Facilitates Greater Accuracy and Longer Fixation Time on Suspicious Areas – via Diagnostic Imaging
- AI system found to boost mammo lesion detection without increasing reading time – via HealthCareBusiness
- AI helps radiologists keep an eye on breast cancer detection – via Cosmos Magazine
