Augmenting Telepostpartum Care With Vision-Based Detection of Breastfeeding-Related Conditions: Algorithm Development and Validation.

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.

A calibration method for smartphone camera photophlethysmography.

Smartphone camera photoplethysmography (cPPG) enables non-invasive pulse oximetry and hemoglobin concentration measurements. However, the aesthetic-driven non-linearity in default image capture and preprocessing pipelines poses challenges for consistency and transferability of cPPG across devices. This work identifies two key parameters-tone mapping and sensor threshold-that significantly impact cPPG measurements. We propose a novel calibration method to linearize camera measurements, thus enhancing consistency and transferability of cPPG across devices. A benchtop calibration system is also presented, leveraging a microcontroller and LED setup to characterize these parameters for each phone model. Our validation studies demonstrate that, with appropriate calibration and camera settings, cPPG applications can achieve 74% higher accuracy than with default settings. Moreover, our calibration method proves effective across different smartphone models (N=4), and calibrations performed on one phone can be applied to other smartphones of the same model (N=6), enhancing consistency and scalability of cPPG applications.

Investigating interactive methods in remote chestfeeding support for lactation consulting professionals in Brazil.

Objective: Lactation consultants (LCs) positively impact chestfeeding rates by providing in-person support to struggling parents. In Brazil, LCs are a scarce resource and in high demand, risking chestfeeding rates across many communities nationwide. The transition to remote consultations during the COVID-19 pandemic made LCs face several challenges to solve chestfeeding problems due to limited technical resources for management, communication, and diagnosis. This study investigates the main technological issues LCs have in remote consultations and what technology features are helpful for chestfeeding problem-solving in remote settings. Methods: This paper implements qualitative investigation through a contextual study ( n = 10 ) and a participatory session ( n = 5 ) to determine stakeholders' preferences for technology features in solving chestfeeding problems. Findings: The contextual study with LCs in Brazil characterized (1) the current appropriation of technologies that help during consultations, (2) technology limitations that affect LCs' decision-making, (3) challenges and benefits of remote consultations, and (4) cases that are easy and difficult to solve remotely. The participatory session brings LCs' perceptions on (1) components for an effective remote evaluation, (2) preferred elements by professionals when providing remote feedback to parents, and (3) feelings about using technology resources for remote consultations. Conclusion: Findings suggest that LCs adapted their methodologies for remote consultations, and the perceived benefits of this modality show interest in continuing to provide remote care as long as more integrative and nurturing applications are offered to their clients. We learned that fully remote lactation care might not be the main objective for overall populations in Brazil, but as a hybrid mode of care that benefits parents by having both modalities of consultations available to them. Finally, remote support helps reduce financial, geographic, and cultural barriers in lactation care. However, future research must identify how generalized solutions for remote lactation care can be, especially for different cultures and regions.

At-Home Pupillometry using Smartphone Facial Identification Cameras.

With recent developments in medical and psychiatric research surrounding pupillary response, cheap and accessible pupillometers could enable medical benefits from early neurological disease detection to measurements of cognitive load. In this paper, we introduce a novel smartphone-based pupillometer to allow for future development in clinical research surrounding at-home pupil measurements. Our solution utilizes a NIR front-facing camera for facial recognition paired with the RGB selfie camera to perform tracking of absolute pupil dilation with sub-millimeter accuracy. In comparison to a gold standard pupillometer during a pupillary light reflex test, the smartphone-based system achieves a median MAE of 0.27mm for absolute pupil dilation tracking and a median error of 3.52% for pupil dilation change tracking. Additionally, we remotely deployed the system to older adults as part of a usability study that demonstrates promise for future smartphone deployments to remotely collect data in older, inexperienced adult users operating the system themselves.