Back to Research

Practical Considerations and Technical Guidelines for Acquiring Clinical Images for Lactation-related Concerns

This page is under construction

Poster ILCA 2025

Abstract

Artificial intelligence can potentially transform lactation care, helping to educate patients and providers and identify breastfeeding complications, particularly in low-resource settings. High-quality images are needed to inform accurate and reliable algorithms. Guidelines for high-quality image capture in clinical settings are required to inform algorithm fairness, reliability of results, and user safety.

Research aim: This study aims to: (a) detail prior studies focused on detecting breastfeeding complications using AI, (b) outline key image quality factors that influence the performance (accuracy and reliability) of AI tools used in lactation care, (c) consult IBCLC experts to inform guidelines for clinical image capture that enhance AI classification of lactation complications.

Learning Objectives:

Identify key factors in clinical image acquisition that influence outcomes in image-based AI tools for lactation care.
Describe best practices for capturing and selecting breast images of lactating patients in clinical settings to ensure dataset quality and results.
Interpret expert-informed guidelines to support the creation of datasets that enable fair, reliable, and safe AI development for lactation care.

References

Kühnisch, J., Meyer, O., Hesenius, M., Hickel, R., & Gruhn, V. (2022). Caries detection on intraoral images using artificial intelligence. Journal of dental research, 101(2), 158-165.
Menzies, S. W., Sinz, C., Menzies, M., Lo, S. N., Yolland, W., Lingohr, J., ... & Kittler, H. (2023). Comparison of humans versus mobile phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: a multicentre, prospective, diagnostic, clinical trial. The Lancet Digital Health, 5(10), e679-e691.
Papachristou, P., Söderholm, M., Pallon, J., Taloyan, M., Polesie, S., Paoli, J., ... & Falk, M. (2024). Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial. British Journal of Dermatology, 191(1), 125-133.
Muthusamy, V. R., Wani, S., Gyawali, C. P., Komanduri, S., Bergman, J., Canto, M. I., ... & Yapp, R. G. (2022). AGA clinical practice update on new technology and innovation for surveillance and screening in Barrett’s esophagus: expert review. Clinical Gastroenterology and Hepatology, 20(12), 2696-2706.
Shih, P. E., & Oh, D. H. (2025). Artificial Intelligence for Dermatological Image Quality Assessment. Current Dermatology Reports, 14(1), 23.
Shahzil, M., Kashif, T. B., Jamil, Z., Khaqan, M. A., Munir, L., Amjad, Z., ... & Enofe, I. (2026). Assessing the effectiveness of texture and color enhancement imaging versus white‐light endoscopy in detecting gastrointestinal lesions: A systematic review and meta‐analysis. DEN open, 6(1), e70128.
Finnane, A., Curiel-Lewandrowski, C., Wimberley, G., Caffery, L., Katragadda, C., Halpern, A., ... & Soyer, H. P. (2017). Proposed technical guidelines for the acquisition of clinical images of skin-related conditions. JAMA dermatology, 153(5), 453-457.
Daneshjou, R., Barata, C., Betz-Stablein, B., Celebi, M. E., Codella, N., Combalia, M., ... & Rotemberg, V. (2022). Checklist for evaluation of image-based artificial intelligence reports in dermatology: CLEAR derm consensus guidelines from the international skin imaging collaboration artificial intelligence working group. JAMA dermatology, 158(1), 90-96.
Abbott, L. M., Miller, R., Janda, M., Bennett, H., Taylor, M., Arnold, C., ... & Caffery, L. J. (2020). Practice guidelines for teledermatology in Australia. Australasian Journal of Dermatology, 61(3), e293-e302.
Dahlén Gyllencreutz, J., Johansson Backman, E., Terstappen, K., & Paoli, J. (2018). Teledermoscopy images acquired in primary health care and hospital settings–a comparative study of image quality. Journal of the European Academy of Dermatology and Venereology, 32(6), 1038-1043.
De Souza, J., Calsinski, C., Chamberlain, K., Cibrian, F., & Wang, E. J. (2023). Investigating interactive methods in remote chestfeeding support for lactation consulting professionals in Brazil. Frontiers in Digital Health, 5, 1143528.
De Souza, J., Chamberlain, K., & Wang, E. J. (2024, May). LCBuddy: Towards a smartphone-based self-assessment tool for postpartum patients with breast pain. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-7).
Nakamura, M., Sugimori, H., & Ebina, Y. (2025). Development of nipple trauma evaluation system with deep learning. Journal of Human Lactation, 41(1), 105-114.
De Souza, J., Viswanath, V. K., Echterhoff, J. M., Chamberlain, K., & Wang, E. J. (2024). Augmenting Telepostpartum Care With Vision-Based Detection of Breastfeeding-Related Conditions: Algorithm Development and Validation. JMIR AI, 3(1), e54798.
De Souza, J., Coca, K. P., Camargo, B. T. S., Chamberlain, K., de Vilhena Abrão, A. C. F., Kusahara, D. M., & Wang, E. J. (2025). Automated Detection and Classification of Nipple Damage in Lactation Care. (Under revision, NPJ Women's Health)

Page updated

Google Sites

Report abuse