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Background: Machine learning in computer-assisted diagnostics improves sensitivity of image analysis and reduces time and effort for interpretation. Compared to standard mammograms, a thermal scan is easily scalable and is a safer screening tool. We evaluate the performance of an Artificial Intelligence (AI) based thermographic screening tool) compared with other standard breast cancer screening modalities Objectives: To evaluate the effectiveness of AI based Thermal mammography compared with standard screening modalities in suspected breast cancer patients. Methodology: A prospective multicentre study was conducted to assess the non-inferiority of sensitivity of AI based Thermal mammography (Thermalytix) to that of standard modalities in detecting malignancy in subjects who show possible symptoms of suspected breast cancer. Standard screening modalities and Thermalytix were obtained and interpreted independently in a blinded fashion. A receiver operating characteristic (ROC) curve was constructed to identify the best cut-off point, non-inferiority margin of =10% to demonstrate the non-inferiority. Results: We recruited 314 symptomatic women who first underwent a thermal scan, followed by mammogram and/or ultrasound. At Youden’s Index of ROC curve, the test device had a sensitivity of 82.5% (95% CI 73.2 to 91.9) and specificity of 80.5% (95% CI 75.0 to 86.1) as compared with diagnostic mammogram, which had sensitivity of 92% (95% CI 80.7 to 97.8) and specificity of 45.9% (95% CI 34.3 to 57.9). The overall area under the curve (AUC) was 0.845. Conclusion: The high AUC in both women under 45 years and above 45 years shows the potential of Thermalytix to be a supplemental diagnostic modality for all ages
Prabhu et al. (Mon,) studied this question.