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AIM for Breast Thermography

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Artificial Intelligence in Medicine

Abstract

The use of Artificial Intelligence (AI) in medicine has increased in the recent years due to its ability to aid the clinician for better clinical decision. AI solutions could play a critical role in the growing need for health care access and low clinician to population ratio. Breast cancer incidence rate is rising every year and there is an urgent need for scalable and low-cost solutions for breast cancer detection, especially for low and middle income countries. Breast thermography has been used as an adjunct modality for breast cancer detection since 1980s. Breast thermography has advantages of being low-cost, non-invasive, radiation free, and privacy aware. However, subjectivity and high expertise required for interpretation of breast thermograms are major challenges for its poor adaptability and equivocal results in the literature for its effectiveness of breast cancer detection. To overcome these challenges, AI solutions have been proposed to automate different steps involved in breast thermography. The recent explorations have suggested that automated breast thermography can play a potential role in breast cancer detection, risk estimation, and hormonal status prediction. In this chapter, we discuss breast thermography and different AI applications in automated breast thermography.

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Acknowledgements

We would like to thank Prof. Andre Dekker and Prof. Leonard Wee from Maastricht University, Netherlands, for their valuable suggestions and comments on the work.

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Authors and Affiliations

  1. Niramai Health Analytix Private Limited, Bangalore, India

    Siva Teja Kakileti & Geetha Manjunath

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  1. Siva Teja Kakileti
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  2. Geetha Manjunath
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Correspondence to Siva Teja Kakileti .

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Editors and Affiliations

  1. Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden

    Niklas Lidströmer

  2. Imperial College London, London, UK

    Hutan Ashrafian

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Kakileti, S.T., Manjunath, G. (2022). AIM for Breast Thermography. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-64573-1_251

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  • DOI: https://doi.org/10.1007/978-3-030-64573-1_251

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