Automated Detection and Classification of Nipple Damage in Lactation Care

Lactation-related nipple damage is a prevalent issue among breastfeeding mothers, often leading to early breastfeeding cessation due to pain and misdiagnosis. Accurate and timely classification of nipple damage is critical for effective treatment, yet current methods rely on subjective clinical assessments, resulting in variability and inefficiency. This study addresses these challenges by developing a Deep Learning (DL) system for the automated detection and classification of nipple damage. Using a dataset of 1,090 images from clinical trials developed in São Paulo, Brazil, we implemented a Resnet50 convolutional neural network (CNN) to perform two tasks: (1) binary classification to differentiate between intact nipples and those with damages and (2) multiclass classification to identify four types of damage (closed wound, crust, erosion, and fissure) based on the instrument for classifying nipple and areola complex lesions. Data augmentation techniques were applied to upsample the dataset to 8,720 images. The binary classification model achieved an average area under the receiver operating characteristics curve (AUROC) of 0.99 and a recall of 95.90%, demonstrating high accuracy in detecting nipple damage. The multiclass model achieved AUROC values ranging from 0.89 to 0.99 in nipple damage classification, with the highest performance observed for closed wounds (AUROC = 0.98) and erosion (AUROC = 0.99). Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed the model’s focus on damaged areas, which aligned closely with clinical assessments. Our findings highlight the potential of DL to improve lactation care by enabling accurate, automated nipple damage classification, particularly in settings with limited access to lactation specialists. This study represents a significant step toward leveraging technology to address challenges in lactation care and improve outcomes for breastfeeding mothers.