JMIR Publications

ABSTRACT

Background:

Breastfeeding benefits both mother and infant and is a topic of attention in public health. After childbirth, untreated medical conditions or lack of support lead many mothers to discontinue breastfeeding. For instance, nipple damage and mastitis affect 80% and 20% of US mothers, respectively. Lactation Consultants (LCs) help mothers with breastfeeding, providing in-person, remote, and hybrid lactation support. LCs guide, encourage and find ways for mothers to have a better experience breastfeeding. Current telehealth services help mothers seek LCs for breastfeeding support, where images help them identify and address many issues. Due to the disproportional ratio of LCs and mothers in need, these professionals are often overloaded and burned out.

Objective:

We investigate the effectiveness of five distinct convolution neural networks (CNNs) in detecting healthy lactating breasts and six breastfeeding-related issues by only using RGB images. Our goal is to assess the applicability of this algorithm as an auxiliary resource for LCs to identify painful breast conditions quickly, better manage their patients through triage, respond promptly to patient needs, and enhance the overall experience and care for breastfeeding mothers.

Methods:

We evaluate the potential for five classification models to detect breastfeeding-related conditions using 1,078 breast and nipple images gathered from online and physical educational resources. We used the CNNs Resnet50, VGG16, InceptionV3, EfficientNetV2, and DenseNet169 to classify the images across seven classes: healthy, abscess, mastitis, nipple blebs, dermatosis, engorgement, and nipple damage by improper feeding or misuse of breast pumps. We also evaluate the models’ ability to identify between healthy and unhealthy images. We present an analysis of the classification challenges, identifying image traits that may confound the detection model.

Results:

The best model achieves an average area under the ROC curve (AUC) of 0.93 for all conditions after data augmentation for multi-class classification. For binary classification, we achieved with the best model an average AUC of 0.96 for all conditions after data. Several factors contributed to the misclassification of images, including (1) similar visual features in the conditions that precede other conditions (such as the mastitis spectrum disorder), (2) partially covered breasts and/or nipples, and (3) images depicting multiple conditions in the same breast.

Conclusions:

This vision-based automated detection technique offers an opportunity to enhance postpartum care for mothers and can potentially help alleviate the workload of LCs by expediting decision-making processes.