Development and validation of an infrared-artificial intelligence software for breast cancer detection

• Published in: Exploration of targeted anti-tumor therapy Vol. 4; no. 2; pp. 294 - 306
• Main Authors: Martín-Del-Campo-Mena, Enrique, Sánchez-Méndez, Pedro A, Ruvalcaba-Limon, Eva, Lazcano-Ramírez, Federico M, Hernández-Santiago, Andrés, Juárez-Aburto, Jorge A, Larios-Cruz, Kictzia Y, Hernández-Gómez, L Enrique, Merino-González, J Andrei, González-Mejía, Yessica
• Format: Journal Article
• Language: English
• Published: United States Open Exploration 2023; Open Exploration Publishing Inc
• Subjects: artificial intelligence; breast cancer; infrared thermography; mammography; Original; screening; infrared thermography; screening; Breast cancer; mammography; artificial intelligence
• ISSN: 2692-3114; 2692-3114
• DOI: 10.37349/etat.2023.00135

In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is recognized as an adjunctive technique for the detection of BC due to its advantages such as safety (by not emitting ionizing radiation nor applying any stress to the breast), portability, and low cost. Improved by advanced computational analytics techniques, infrared thermography could be a valuable complementary screening technique to detect BC at early stages. In this work, an infrared-artificial intelligence (AI) software was developed and evaluated to help physicians to identify potential BC cases.
Several AI algorithms were developed and evaluated, which were learned from a proprietary database of 2,700 patients, with BC cases that were confirmed through mammography, ultrasound, and biopsy. Following by evaluation of the algorithms, the best AI algorithm (infrared-AI software) was submitted to a clinic validation process in which its ability to detect BC was compared to mammography evaluations in a double-blind test.
The infrared-AI software demonstrated efficiency values of 94.87% sensitivity, 72.26% specificity, 30.08% positive predictive value (PPV), and 99.12% negative predictive value (NPV), whereas the reference mammography evaluation reached 100% sensitivity, 97.10% specificity, 81.25% PPV, and 100% NPV.
The infrared-AI software here developed shows high BC sensitivity (94.87%) and high NPV (99.12%). Therefore, it is proposed as a complementary screening tool for BC.