背景:母乳喂养对母亲和婴儿都有好处,是公共卫生关注的话题。分娩后,未经治疗的医疗条件或缺乏支持导致许多母亲停止母乳喂养。例如,乳头损伤和乳腺炎影响80%和20%的美国母亲,分别。哺乳顾问(LCs)帮助母亲母乳喂养,亲自提供,远程,和杂交哺乳支持。LCs指南,鼓励,并为母亲找到更好的母乳喂养体验的方法。目前的远程医疗服务帮助母亲寻求LCs的母乳喂养支持,图像帮助他们识别和解决许多问题。由于LCs和有需要的母亲的比例不成比例,这些专业人员经常超负荷工作,精疲力竭。
目的:本研究旨在调查5种不同的卷积神经网络在检测健康泌乳乳房和6种母乳喂养相关问题中的有效性,绿色,和蓝色图像。我们的目标是评估该算法作为LCs的辅助资源的适用性,以快速识别疼痛的乳房状况。通过分诊更好地管理病人,及时响应患者需求,并增强母乳喂养母亲的整体体验和护理。
方法:我们使用从网络和体育教育资源收集的1078张乳房和乳头图像,评估了5种分类模型检测母乳喂养相关状况的潜力。我们使用卷积神经网络Resnet50,16层视觉几何组模型(VGG16),InceptionV3、EfficientNetV2和DenseNet169将图像分类为7类:健康、脓肿,乳腺炎,乳头水泡,皮肤病,充血,和乳头损坏不当喂养或误用吸奶器。我们还评估了模型区分健康和不健康图像的能力。我们对分类挑战进行了分析,识别可能混淆检测模型的图像特征。
结果:最佳模型在进行多类别分类的数据增强后,对于所有条件,接收器工作特征曲线下的平均面积均为0.93。对于二元分类,我们实现了,用最好的模型,数据增加后,所有条件的曲线下平均面积为0.96。有几个因素导致了图像的错误分类,包括在其他条件(如乳腺炎谱系障碍)之前的条件类似的视觉特征,部分覆盖的乳房或乳头,和描绘同一乳房中多种情况的图像。
结论:这种基于视觉的自动检测技术为加强母亲的产后护理提供了机会,并有可能通过加快决策过程来帮助减轻LCs的工作量。
BACKGROUND: Breastfeeding benefits both the 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: This study aims to investigate the effectiveness of 5 distinct convolutional neural networks in detecting healthy lactating breasts and 6 breastfeeding-related issues by only using red, green, and blue images. Our goal was 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 evaluated the potential for 5 classification models to detect breastfeeding-related conditions using 1078 breast and nipple images gathered from web-based and physical educational resources. We used the convolutional neural networks Resnet50, Visual Geometry Group model with 16 layers (VGG16), InceptionV3, EfficientNetV2, and DenseNet169 to classify the images across 7 classes: healthy, abscess, mastitis, nipple blebs, dermatosis, engorgement, and nipple damage by improper feeding or misuse of breast pumps. We also evaluated the models\' ability to distinguish 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 receiver operating characteristic curve of 0.93 for all conditions after data augmentation for multiclass classification. For binary classification, we achieved, with the best model, an average area under the curve of 0.96 for all conditions after data augmentation. Several factors contributed to the misclassification of images, including similar visual features in the conditions that precede other conditions (such as the mastitis spectrum disorder), partially covered breasts or nipples, and 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.