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Development of Nipple Trauma Evaluation System With Deep Learning
Maya Nakamura1, Hiroyuki Sugimori2, Yasuhiko Ebina2
1Graduate School of Health Sciences, Hokkaido University, Sapporo, Japan.
Background:
No research has been conducted on the use of deep learning for breastfeeding support.
Research Aim:
This study aims to develop a nipple trauma evaluation system using deep learning.
Methods:
We used an exploratory data analysis approach to develop a deep-learning model for medical imaging. Employing object detection and classification, this Japanese study retrieved 753 images from a previous study. The classification protocol, based on the "seven signs of nipple trauma associated with breastfeeding," categorized the images into eight classes. For practical purposes, the eight original classes were consolidated into four broader categories: "None," "Minor," "Moderate," and "Severe," using data augmentation procedures that were consistent with the original classification system. The Precision, Recall, Overall Accuracy, and Area Under the Curve (AUC) were calculated, and the model's efficiency was evaluated using Frames Per Second (FPS).
Results:
The object detector's high mean average precision and frames per second rate for nipple and areola detection, confirmed exceptional accuracy. The eight-class image classifier returned notable AUC values, with fissures, peeling, purpura, and scabbing exceeding 0.8. The highest average recall and precision was for scabbing, and the lowest for blistering. The four-class classifier accurately predicted severe conditions, with an average AUC > 0.7, whereas categories without classifications and those deemed minor had lower recall and precision rates.
Conclusions:
A sophisticated deep learning system detects and classifies nipple trauma automatically, potentially aiding breastfeeding caregivers through objective image assessment and operational improvements.
Abstract In Japanese:
： におけるのにするはわれていない。： は、をいたシステムのをとした。： では、をいたモデルをするため、データアプローチをいた。およびのをい、でわれたでされた753のをした。「にうの７」にづき、を8クラスにした。をし、4つのカテゴリ「なし」、「」、「」、「」の4つのカテゴリにし、のシステムにするデータをった。、、Overall Accuracy、AUC（）をし、モデルのはFPS（Frames Per Second）でした。： におけるいmAP（）とFPSがされ、およびのがされた。8クラスのは、、、、で0.8をえるなAUCがられた。とがもかったのはであり、でもかった。4クラスのはのをにし、AUCは0.7をえたが、なしやとされるカテゴリはとがいとなった。： をしたこのなシステムは、のとをでうことができ、なをじて、のとをサポートするなツールとなりる。Back Translation Completed by Hiroko Hongo, MSW, PhD, IBCLC.
