A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital

Richa Bansal; Sathiakar Collison; Lakshmi Krishnan; Bharat Aggarwal; Mathukumalli Vidyasagar; Siva Teja Kakileti; Geetha Manjunath

doi:10.3389/frai.2022.1050803

A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital

Front Artif Intell. 2023 Jan 4:5:1050803. doi: 10.3389/frai.2022.1050803. eCollection 2022.

Authors

Richa Bansal¹, Sathiakar Collison², Lakshmi Krishnan², Bharat Aggarwal¹, Mathukumalli Vidyasagar³, Siva Teja Kakileti², Geetha Manjunath²

Affiliations

¹ Radiology Services, Max Super Speciality Hospital, Saket, New Delhi, India.
² Department of Clinical Affairs, Niramai Health Analytix Pvt. Ltd., Bangalore, India.
³ SERB National Science Chair, Indian Institute of Technology Hyderabad, Kandi, India.

Abstract

Objective: Artificial intelligence-enhanced breast thermography is being evaluated as an ancillary modality in the evaluation of breast disease. The objective of this study was to evaluate the clinical performance of Thermalytix, a CE-marked, AI-based thermal imaging test, with respect to conventional mammography.

Methods: A prospective, comparative study performed between 15 December 2018 and 06 January 2020 evaluated the performance of Thermalytix in 459 women with both dense and nondense breast tissue. Both symptomatic and asymptomatic women, aged 30-80 years, presenting to the hospital underwent Thermalytix followed by 2-D mammography and appropriate confirmatory investigations to confirm malignancy. The radiologist interpreting the mammograms and the technician using the Thermalytix tool were blinded to the others' findings. The statistical analysis was performed by a third party.

Results: A total of 687 women were recruited, of whom 459 fulfilled the inclusion criteria. Twenty-one malignancies were detected (21/459, 4.6%). The overall sensitivity of Thermalytix was 95.24% (95% CI, 76.18-99.88), and the specificity was 88.58% (95% CI, 85.23-91.41). In women with dense breasts (n = 168, 36.6%), the sensitivity was 100% (95% CI, 69.15-100), and the specificity was 81.65% (95% CI, 74.72-87.35). Among these 168 women, 37 women (22%) were reported as BI-RADS 0 on mammography; in this subset, the sensitivity of Thermalytix was 100% (95% CI, 69.15-100), and the specificity was 77.22% (95% CI, 69.88-83.50).

Conclusion: Thermalytix showed acceptable sensitivity and specificity with respect to mammography in the overall patient population. Thermalytix outperformed mammography in women with dense breasts and those reported as BI-RADS 0.

Keywords: artificial intelligence; breast density; mammography; prospective studies; thermography.