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Radiology: Artificial Intelligence , vol. 2, no. 6, p. e190208, Nov. 2020
Multi-reader multi-case study to demonstrate the benefits brought by MammoScreen in the breast cancer detection process. Results have shown that the average AUC across readers was 0.769 (95% confidence interval [CI]: 0.724, 0.814) without AI and 0.797 (95% CI: 0.754, 0.840) with AI. The average difference in AUC was 0.028 (95% CI: 0.002, 0.055, P = .035). Average sensitivity was increased by 0.033 when using AI support (P = .021). Reading time changed dependently to the AI-tool score. For low likelihood of malignancy (< 2.5%) the time was about the same in the first reading session and slightly decreased in the second reading session. For higher likelihood of malignancy, the reading time was on average increased with the use of AI. We concluded that the concurrent use of this AI tool improves the diagnostic performance of radiologists in the detection of breast cancer without prolonging their workflow.
Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 1151321 (22 May 2020)
This study was aimed to compare the performances of MammoScreen and another deep learning based software against three radiologists ona recall-based model for mammography. A set of examinations from a daily practice, with both screening and diagnostic studies, has been interpreted by the radiologists and the two AI based algorithms. The dataset has been enriched with BIRADS 4 and 5 cases in order to have a number of cancer cases sufficient to have statistically significant results. In total, 140 examinations have been included in the final dataset. Sensitivity, False positive rate (FPR), and recall rate per BI-RADS category were considered as endpoints for each of the radiologists. While both the algorithms and radiologists have a good and comparative rate of sensitivity and FPR, the test based on BI-RADS categories (i.e. the number of cancer per BI-RADS category), showed heterogeneous results, with bad performances for one of the tested software on the extremes score of BI-RADS. Conclusions of this study say that one of the analysed software cannot be used in the current clinical practice without further improvements, MammoScreen shows promising results, but other studies are needed to have a robust external validation before being used in a daily practice.
ECR 2020 Book of Abstracts. Insights Imaging 11, 34 (2020).
Multi-reader multi-case study to demonstrate the benefits brought by MammoScreen in the breast cancer detection process. Results have shown that the average AUC across readers was 0.769 (95% confidence interval [CI]: 0.724, 0.814) without AI and 0.797 (95% CI: 0.754, 0.840) with AI. The average difference in AUC was 0.028 (95% CI: 0.002, 0.055, P = .035). Average sensitivity was increased by 0.033 when using AI support (P = .021). Reading time changed dependently to the AI-tool score. For low likelihood of malignancy (< 2.5%) the time was about the same in the first reading session and slightly decreased in the second reading session. For higher likelihood of malignancy, the reading time was on average increased with the use of AI. We concluded that the concurrent use of this AI tool improves the diagnostic performance of radiologists in the detection of breast cancer without prolonging their workflow.
JAMA Netw Open. 2020 Mar; 3(3): e200265.
The common final paper written together by the Organizers and Participants of the Digital Mammography DREAM Challenge where Therapixel was ranked 1st. The Dream Challenge has been described as the largest data challenge to date focused on mammographic imaging. This paper evaluates the AI models submitted for mammography analysis. The ensemble of models created by the Top-8 teams were evaluated on two blind datasets from the US and Sweden, in general they performed coherently across the different datasets. While the AI ensemble still underperformed with respect to a dedicated fellowship trained mammographer, it did however demonstrate that radiologists working in conjunction with the AI assessments lead to a statistically significant improvement of sensitivity, from 90.5% (radiologist only) to 92% (radiologist with AI). In the US, this would have reduced the number of women requiring unnecessary diagnostic workup by more than half a million. Since the challenge Therapixel has significantly improved its AI models by more than doubling the amount of data used to initially develop the MammoScreen system.