Monitoring hormonal changes during pregnancy, lactation, and menopause is essential for women's health diagnostics and treatment planning, yet no clinically established imaging modality exists for routine, noninvasive hormonal monitoring. Hormonal fluctuations influence peripheral vasodilation and thermoregulation, potentially generating measurable skin temperature patterns. In this paper, we present MV-HCNN (Multiview Hormonal Convolutional Neural Network), a novel multiview infrared imaging and deep learning system for noninvasive classification of hormonal states in women. The system integrates a protocol-compliant long-wave infrared (LWIR) camera, a standardized five-view breast acquisition protocol, and a multiview CNN that fuses view-specific features with cross-view global correlations for robust classification. MV-HCNN is trained and validated on thermal images from 105 376 women and evaluated on an independent dataset of 47 220 women in a prospective-like setting. Hormonal states (menopause, pregnancy, lactation) were self-reported via structured interviews, and age-matched analyses were performed to reduce confounding. The proposed MV-HCNN achieved area under the ROC curves (AUCs) of 0.98, 0.91, and 0.91 for pregnancy, lactation, and menopause, respectively, outperforming a radiomics-based baseline across all tasks. Performance remained stable in age-matched groups except for menopause, which showed a moderate decline (AUC = 0.77) due to physiological variability during the menopausal transition. This study provides large-scale evidence that MV-HCNN, a portable, radiation-free, multiview LWIR imaging system leveraging both view-specific features and cross-view global correlations, can differentiate physiological hormonal states, enabling scalable, noninvasive longitudinal monitoring for women's health.

Background Mammographic screening performance declines in women with dense breasts, leading to diagnostic inequities and delayed cancer detection. Thermal imaging captures complementary functional cues such as vascular and metabolic activity that remain detectable irrespective of density with the help of Artificial Intelligence (AI). This study presents an adaptive Density-Informed Multimodal AI (DIMA) Framework that integrates mammography and thermal imaging to improve breast cancer detection performance across breast density categories. Methods The framework integrates two complementary AI pipelines: a multi-view deep learning model trained on 19,883 mammograms for morphological feature analysis, and a radiomics-based Thermalytix system trained on over 100,000 thermal images to capture vascular and thermal asymmetries. Breast density, categorized by ACR grades, functions as a conditional variable that dynamically determines which model’s prediction is used. The mammography AI is applied for fatty breasts (ACR A and B), whereas the Thermalytix AI is prioritized for dense breasts (ACR C and D). This density-conditioned decision logic enables optimal modality utilization while maintaining interpretability. To assess real-world applicability, the framework was evaluated on 324 women who underwent both mammography and thermal imaging. Results The DIMA framework achieved a sensitivity of 94.6% (95% CI: 88.6–100) and specificity of 79.9% (95% CI: 75.1–84.7), outperforming standalone mammography AI (sensitivity 81.8%, specificity 86.3%) and Thermalytix AI (sensitivity 92.7%, specificity 75.5%). Importantly, the sensitivity of Mammography dropped significantly in dense breasts (67.9%) versus fatty breasts (96.3%), whereas Thermalytix AI maintained high and consistent sensitivity in both (92.6% and 92.9%, respectively). Conclusions This retrospective cross-sectional diagnostic accuracy evaluation demonstrates the potential of a DIMA routing strategy to improve breast cancer detection across breast density categories. Population-based screening studies are required to assess its generalizability, equity, and role within large-scale screening programs.

Breast tissue density is an established biomarker of breast cancer risk and an important determinant of mammographic sensitivity. Density assessment is typically performed using X-ray mammography. In this study, we investigate whether breast tissue density–related information may be reflected in infrared thermal images using artificial intelligence. The underlying hypothesis is that fibroglandular and adipose tissues differ in thermophysical and physiological properties, potentially giving rise to subtle surface temperature patterns. We propose DensiThAI, a multi-view deep learning framework that integrates information from five standardized thermal breast views to classify density. The framework was evaluated on a multi-center dataset of 3500 women using mammography-derived breast density labels as the reference standard. DensiThAI achieved a mean AUROC of 0.73 across 10 independent test splits, with statistically significant separation between density classes (p < 0.05). These findings suggest that density-associated thermal patterns may be detectable and motivate further investigation of thermal imaging as a complementary modality for breast tissue characterization in larger and independently validated cohorts.

Breast tissue density is a key biomarker of breast cancer risk and a major factor affecting mammographic sensitivity. However, density assessment currently relies almost exclusively on X-ray mammography, an ionizing imaging modality. This study investigates the feasibility of estimating breast density using artificial intelligence over infrared thermal images, offering a non-ionizing imaging approach. The underlying hypothesis is that fibroglandular and adipose tissues exhibit distinct thermophysical and physiological properties, leading to subtle but spatially coherent temperature variations on the breast surface. In this paper, we propose DensiThAI, a multi-view deep learning framework for breast density classification from thermal images. The framework was evaluated on a multi-center dataset of 3,500 women using mammography-derived density labels as reference. Using five standard thermal views, DensiThAI achieved a mean AUROC of 0.73 across 10 random splits, with statistically significant separation between density classes across all splits (p << 0.05). Consistent performance across age cohorts supports the potential of thermal imaging as a non-ionizing approach for breast density assessment with implications for improved patient experience and workflow optimization.

Mammography, the current standard for breast cancer screening, has reduced sensitivity in women with dense breast tissue, contributing to missed or delayed diagnoses. Thermalytix, an AI-based thermal imaging modality, captures functional vascular and metabolic cues that may complement mammographic structural data. This study investigates whether a breast density-informed multi-modal AI framework can improve cancer detection by dynamically selecting the appropriate imaging modality based on breast tissue composition. A total of 324 women underwent both mammography and thermal imaging. Mammography images were analyzed using a multi-view deep learning model, while Thermalytix assessed thermal images through vascular and thermal radiomics. The proposed framework utilized Mammography AI for fatty breasts and Thermalytix AI for dense breasts, optimizing predictions based on tissue type. This multi-modal AI framework achieved a sensitivity of 94.55

Introduction: Breast pain is the most common breast complaint presented to general practitioners (GPs) and the leading cause of referrals to breast units. Although the correlation between breast pain and breast cancer is low, the high volume of these referrals often results in over-investigation, potentially delaying the diagnosis and treatment of women with actual breast cancer. Thermalytix is an artificial intelligence-based breast imaging tool that uses advanced machine learning over high resolution thermal scans to generate a breast cancer risk score (B-Score). In this study, we assess whether Thermalytix can be utilized to effectively manage patients presenting with breast pain, thereby reducing subjectivity in referrals to upstream diagnostic pathways. Methodology: This is a post-hoc analysis of data from prior clinical studies of Thermalytix, involving a total of 1187 women. The reference standard was the final diagnosis obtained from the available reports of mammography, ultrasound, and biopsy. Exclusion criteria included women below 18 years of age, women with a history of breast cancer, and those pregnant or lactating. For this analysis, women presenting with breast pain alone as a complaint was used as inclusion criterion. The performance of Thermalytix was evaluated within this subset of the population using B-Score for triaging. Further, we estimated the optimal criterion for vascular score (an output generated by Thermalytix to characterize the vascular asymmetry in the breasts) for improving B-score computation of Thermalytix using a receiver operating characteristic curve. Results: Of the 1187 women, 157 (13.2%) women had reported pain alone as a complaint. Of these 157 women, 13 women were diagnosed with breast cancer. Thermalytix resulted in a sensitivity, specificity, NPV and PPV of 76.9%, 85.4%, 97.6%, and 32.3% respectively, using B-Score. When the vascularity criterion is set to the optimal point at Youden’s Index, the sensitivity, specificity, NPV and PPV were 92.3%, 80.6%, 99.1% and 30%, respectively. Conclusion: Currently, management of women presenting with breast pain complaints in primary care requires clinical judgement towards a 2 week wait time. In this paper, we present a non-invasive imaging tool called Thermalytix that can be used for triaging women with breast pain in an objective manner. Acknowledgements: We would like to thank Dr. Deepak Kumar and Dr. Sanjiv Ahluwalia for their valuable discussions and feedback on this study. Citation Format: Siva Teja Kakileti, Geetha Manjunath. AI based Thermalytix for management of Breast Pain in Primary Care [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P4-04-18.

Thermal imaging is emerging as a valuable diagnostic modality in medical applications, offering non-invasive assessment of physiological conditions through temperature mapping. However, the lack of standardized data formats and comprehensive software tools has severely limited clinical adoption and research reproducibility. Here we present MedThermal-DICOM, the first open-source Python framework that provides full compliance with the Digital Imaging and Communications in Medicine (DICOM) standard for thermal imaging. The framework preserves thermal-specific metadata through private extensions, ensures quantitative fidelity via Real World Value Mapping (RWVM), and integrates seamlessly with Picture Archiving and Communication Systems (PACS) and existing clinical workflows. Validation across three public datasets demonstrated syntactic and semantic compliance, quantitative accuracy better than 0.01 ℃, and interoperability with both PACS and RWVM-aware software. By enabling standards-compliant thermal imaging with complete metadata preservation, MedThermal-DICOM provides the technical foundation for reproducible research, multi-center studies, and eventual clinical deployment. The framework is freely available under an open-source license to promote widespread adoption and collaborative development.

Diabetic foot ulceration (DFU) is a severe and prevalent complication of diabetes mellitus, leading to high rates of amputation and mortality. Abnormal plantar foot temperature changes are an early sign of diabetic foot ulcer that can be detected using a thermal camera. This study presents a comprehensive approach to diabetic foot detection using thermal imaging and advanced machine learning techniques. Broadly, the proposed approach comprises a UNet-MobileNetV2 based deep learning architecture to segment the foot region followed by classification of the foot thermal patterns using thermal radiomics. Our approach was evaluated using five-fold cross-validation on both the datasets, addressing the limitations of previous studies that relied on a single dataset. The proposed segmentation model achieved Dice and Intersection over Union (IoU) scores of 97.1

Background: Recent change in mammography reporting guidelines by US FDA highlights the need for an additional supplemental screening modality for women with dense breasts. Furthermore, American College of Radiology updated its screening guidelines recommending annual screening beginning at age 40 for women of average risk and even earlier for women at higherthan-average risk. Even European Guidelines suggest not implementing mammography screening for asymptomatic average-risk women aged 40 to 44 due to breast density issues. A supplemental screening with Breast MRI is not affordable for many countries.

Background Breast cancer is the leading cause of cancer-related deaths among women. Early detection is crucial for improving treatment outcomes and reducing costs. Systematic screening programs using mammography pose significant challenges in developing countries due to high-costs and skill shortages. Thermalytix is an affordable, portable, artificial intelligence (AI) based test that has demonstrated good clinical efficacy and economic feasibility for population-screening. This paper presents insights and data from implementing Thermalytix test on over 100,000 women in India. Methods Thermalytix was deployed at 150 clinical sites and at 1000 + screening camps outside hospitals. All women who took the test with informed consent, in either of these modes, were included to form a diverse cohort of 104,411 women from various socioeconomic backgrounds across 20 Indian-states. Thermalytix AI algorithms analyzed thermal patterns and automatically triaged women into three risk categories (red-yellow-green). Test Positivity Rate (TPR), assuming Red as test-positive, was computed for different cohorts. Results Thermalytix showed a TPR of 6.64% across the entire population. TPR in symptomatic women was 4x higher than in asymptomatic women. Women tested in hospitals exhibited a 1.6x higher TPR than those tested in screening camps. Highest TPR was observed in women aged above 60, followed by those aged 41–50 with complaints and those aged 31–40 without complaints. Postmenopausal women had a higher TPR than premenopausal women. Prior breast cancer led to a higher TPR than those without. Conclusion This study demonstrated the feasibility of implementing Thermalytix for community screening in resource-constrained countries, and the findings correlated with known risk-factors.

The current work investigates the room temperature ethanol gas detection capabilities of pristine, Sn-doped, Zn-doped, Sn & Zn co-doped In _2 O _3 -based screen-printed films, fabricated using particle-free aqueous solution combustible inks on glass substrates. The fabricated films were pure, polycrystalline with cubic bixbyite crystal structure, porous, and transparent (∼75 to 95%) in the visible range. Relatively high surface roughness was detected in pristine film than in doped films. Ethanol gas was detected by all the films at room temperature. Among all, the pristine film showed a relatively greater gas response at all concentrations of ethanol gas ranging from 25 ppm to 100 ppm. This superior gas response was attributed to comparatively greater oxygen vacancy concentration (O _V /O _L ), relative area fraction of surface adsorbed oxygen (% of O _A ), and high surface roughness with porosity. The maximum ethanol gas response attained was ∼17 at 100 ppm concentration by the pristine film, which also demonstrated high selectivity to ethanol gas.

Background: Mammography based breast cancer screening has proven to reduce mortality in women, but is found to be less effective in women with dense breasts. Due to this, different regulatory bodies insisted on the need for having density information on mammography reports in recent years. On the other hand, Thermalytix, a computer aided breast cancer detection system with thermal imaging, is emerging as a new Artificial Intelligence (AI)-based functional imaging that is showing promising results on dense breasts. We propose a multi-modal AI based screening modality that uses both X-Ray mammograms and thermal scans. This is the first ever pilot study that leverages the complementary nature of two imaging modalities.

Abstract Objective: Breast cancer remains a significant health challenge worldwide, particularly among women under 45 years of age who often present with dense breast tissue. Mammography, the current gold standard for breast cancer screening, is less effective in this demographic due to reduced sensitivity in detecting malignancies within dense breast tissue. This study evaluates the efficacy of Thermalytix, an artificial intelligence-driven, noninvasive, and radiation-free thermal imaging tool, as a screening modality for breast cancer in young women through a meta-analysis of published studies. Materials and Methods: This meta-analysis aggregated the data from three clinical studies involving a total of 1187 women who first underwent a Thermalytix test followed by reference standard of care (SoC) tests, which included one or more mammography, ultrasound, and biopsy. Among these women, 463 were under 45 years of age and were eligible for this study. Thermalytix analyzed high-resolution thermal images of the breast, utilizing novel radiomic features such as hotspots, vascular patterns, and areolar characteristics to predict malignancy. The performance of Thermalytix was evaluated by computing its sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), with 95% confidence intervals (95% CIs), in both the overall population and the younger cohort by aggregating the data from all studies. Results: Among the 463 young women under 45 years, 43 were diagnosed with breast cancer as per SoC. When raw data were aggregated from this young women cohort, Thermalytix resulted in a sensitivity of 90.7% (95% CI: 82.0%–99.4%), a specificity of 82.1% (95% CI: 78.5%–85.8%), a PPV of 34.2% (95% CI: 25.5%–42.9%), and an NPV of 98.9% (95% CI: 97.7%–100%). The pooled sensitivity and specificity of Thermalytix using the random-effects model were estimated to be 96.0% (95% CI: 88.9%–100%) and 82.3% (95% CI: 78.6%–85.9%), respectively. Further, in the entire population of 1187, Thermalytix showed an aggregated sensitivity of 88.3% (95% CI: 83.3%–93.2%), specificity of 84.7% (95% CI: 82.5%–86.9%), PPV of 47.7% (95% CI: 42.0%–53.3%), and NPV 97.9% (95% CI: 96.9%–98.8%). Conclusions: Thermalytix demonstrated high sensitivity and NPV in women under 45 years of age, suggesting its potential as an effective screening modality for younger women who face challenges with conventional screening methods due to dense breast tissue.

In this contribution, pure, polycrystalline wurtzite crystal structured, spin-coated pristine ZnO and Ti-doped (1, 2, and 3 wt

Background: Despite improvements in treatment strategies, breast cancer survival rates remain low in India due to a lack of awareness and late stage of presentation. If diagnosed and treated early, breast cancer survival rates improve. Screening mammography, the gold standard in cancer diagnosis, is not feasible in a resource-constrained setting. Niramai’s novel breast cancer screening technology, Thermalytix™, applies Artificial Intelligence (AI) over thermography, to give an automated interpretation of the breast thermal images. The portable Thermalytix test has been so far used to screen 60000 women in community settings across India and Kenya. This study is a recent evaluation of the test in rural setting. Methods: Women who provided written consent and who underwent Thermalytix tests in community-based screening camps at primary health centers (PHCs) in Afzalpur Taluk of Gulbarga District, Karnataka, India between 01 August 2021 to 15 June 2022 were included in this study. Five thermal images in multiple views were analyzed using Niramai’s patented algorithm. Automated analysis of the thermal images produced a screening report and triaged the participants for follow-up. In case of abnormal thermal activity, Thermalytix triaged women as ‘red’ and were referred to the district hospital for follow-up with breast ultrasound and/or other investigations and and were recorded into the following three categories: Normal (BI-RADS 1), Abnormal - benign (BI-RADS 2/3) and Abnormal - malignant (BI-RADS 4/5). If no abnormal thermal patterns were detected by Thermalytix, women would be recommended routine screening. Findings: The analysis included 3531 women were included in the analysis and the median age in the cohort was 42 years. Of them, 97 (2.74%) women were triaged ‘red’ by Thermalytix indicating a suspicion of breast abnormality. As on 15 June 2022, 29 (30%) out of 97 women underwent standard follow-up investigations of which two cases of carcinoma breast, one case of phyllodes, one case of tuberculosis mastitis and seven other benign cases were identified, indicating that Thermalytix has a positive predictive value of 35.71% (11/29) in detecting benign and malignant breast lesions. Furthermore, a Patient experience questionnaire was used to asses their experience. 98.71% women were being screened for breast cancer for the first time in their lives. 93.9% women said they were very satisfied with Thermalytix screening experience and remaining 6.1% said they were satisfied, thus aiding in strong acceptability and adoption of the test. Interpretation: In resource-constrained settings such as India, where less than 2% of the women in the country have ever got screening for breast cancer, the portable, no-radiation Thermalytix test is an accessible breast cancer screening solution. Thermalytix’s patient-friendly features - privacy-aware, painless, comfortable, and radiation-free make it favorable for population screening in India, and thus, can increase the uptake of breast cancer screening. With the device’s capacity to fit into a backpack, it can make breast cancer screening accessible to even remote areas with limited resources. However, following up on women who were found to be Thermalytix positive still remains a challenge. Future studies will emsure that follow-up after abnormal breast screening is part of the approved clinical protocol. Results of the screening program 3 malignancies and 8 benign lesions found in 29 Thermalytix RED patients Citation Format: Geetha Manjunath, Lakshmi Krishnan, Gargi Deshpande, Purnima Madhivanan, Karl Francis Krupp. Analyzing the performance of Thermalytix, an AI-based breast cancer screening solution, in a community setting [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-04-03.

The current work delivers room-temperature ammonia (NH3) gas-detectable pristine, Nb-doped TiO2 air- and vacuum-annealed films obtained through the solution-combustion process. Polycrystalline anatase crystal structured films without any dopant oxide phases were processed at 400°C on glass substrates. The crystallinity was higher in pristine films than in doped films; the morphological features were similar in all the films. The films were > 50

Abstract Background: As opposed to conventional age-based population-level breast screening strategies, risk-stratified breast screening programs are emerging as a new approach to balanced population screening methodology where the screening frequency and choice of modality (mammography/tomosynthesis or magnetic resonance imaging) is determined based on accurate personalized estimation of an individual’s risk score. A woman identified with low risk can now be screened less frequently, avoiding repeated mammography screening where radiation risk outweighs the benefits in that particular individual. The standard questionnaire based risk stratification is found to be less reliable and imaging based risk stratification mechanisms are being explored in recent years. In this study, we evaluate the performance of a new computer-aided image analysis technique called Thermalytix that automatically generates a personalized risk score using a combination of imaging and questionnaire information for risk stratification of women. Methodology: Thermalytix is an artificial intelligence system that uses thermal imaging and questionnaire data for predicting the risk of breast cancer. Thermalytix analyzes spatio-thermal signatures and vascular patterns in the breast region along with patients’ complaints and age to generate a score called B-Score (or BHARATI Score) which ranges from 1 to 5. B-Score of 1 indicates low risk of malignancy and a B-Score of 5 indicates the highest risk of malignancy. To evaluate the effectiveness of risk stratification using B-Scores, we performed retrospective analysis of thermal and participants’ data acquired from two registered clinical studies. One study (CTRI/2017/10/0 10 115) is a multi-site study conducted in Bangalore, India, and the other study (NCT04688086) is a single site study conducted in Delhi, India. Both these study sites are geographically distant with 2000 KM apart from each other and comprise a diversified population from India. Results: In total, 717 eligible women were considered in this study with age varying from 18 years to 80 years. Reports from standard of care procedures involving mammography, ultrasound and biopsy (as needed), were collectively considered by a radiologist to determine the ground truth for malignancy. Out of 717 women, 85 women were thus concluded as malignant. When used in a blinded fashion, Thermalytix graded 275 women as B-Score 1 (lowest risk), 225 women as B-Score 2, 44 women as B-Score 3, 137 women as B-Score 4 and 36 women as B-Score 5 (highest risk). The fraction of malignancies in the cohorts corresponding to B-Score categories from 1 to 5 were found to be progressively higher (0.36%, 1.33%, 29.55%, 33.58% and 61.11%, respectively) - showing the correctness of the proposed personalized risk scoring methodology. Conclusion: Thermalytix test, a low-cost, radiation-free, contactless and privacy aware test was used as a technique to determine the breast cancer risk of a woman.. The results obtained in the study show that a high B-score of 5 indicates a high risk for malignancy with 61.11% chance of breast cancer. Likewise the lowest B-Score of 1 indicates low risk for malignancy with just 0.36% percentage of women in the cohort found with malignancy. These results combined with other experiential benefits of Thermalytix test makes it a promising risk stratification mechanism enabling differential frequency of screening while balancing the cost and risk versus benefit. Large scale studies, however, need to be conducted to see the ground benefits of the proposed approach in a screening program implementation. Distribution of study population in different risk cohorts Higher risk correlates with higher malignancy rate Citation Format: Siva Teja Kakileti, Himanshu Madhu, Richa Bansal, Akshita Singh, Sudhakar Sampangi, Bharat Aggarwal, Geetha Manjunath. An Automated Risk Stratification System for Breast Cancer Screening using Thermalytix [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-25.