Assessment on Adoption Behavior of First-time Mothers on the Usage of Chatbots for Breastfeeding Consultation

Arora, Nikita; Jain, Yash; Gupta, Vaishnavi; Mokashi, Sushil; Panda, Chiranjibi

doi:10.5005/jp-journals-10057-0161

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…But while the majority of newly moms had inadequate knowledge and improper techniques for breastfeeding [19], the simple use of cell phones to send regular and personalized messages to breastfeeding moms, proved to be a very useful tool for frequent and sustained support [20]. However, no sufficient literature on the usage of chatbots in the healthcare domain [21]. In this study, nevertheless, we were able to found two breastfeeding chatbots but not really documented or indexed: Start4Life adopted by UK National Health Service (NHS) [15] and Aleitamento Materno Orientado (AMO), the one and only breastfeeding chatbot indexed in Google Scholar (but not indexed in scopus for example) [16], both using Facebook Messenger as client plateform and developed using Google Dialogflow.…”

Section: Breastfeeding Chatbotsmentioning

confidence: 99%

ALMA: Machine learning breastfeeding chatbot

Achtaich¹,

Achtaich²,

Fagroud³

et al. 2023

Math. Model. Comput.

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Since the first computer, researchers always try to simulate human behave. For Chatbots, one of the first goals is to interact with the user like a human using Natural Language. For Health chatbots, another goal is as much important: be able to provide the correct answer to the user request. Over Years, many health chatbots have been developed for many fields such as cancer, diagnosis orientation, psychiatrics, etc. breastfeeding companion are, however, rare (only two breastfeeding chatbots). In this paper, we have developed ALMA, a Breastfeeding Chatbot (BC) that can converse with a breastfeeding mom throw natural language understanding (NLU) and natural language generation (NLG), and provide her – breastfeeding mom – with the relevant information using AIML knowledge base and CNN pre-trained model. We made ALMA available for a normal WhatsApp conversation throw Twilio API. ALMA was tested by volunteering breastfeeding moms and the results validated by breastfeeding consult.

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Section: Breastfeeding Chatbotsmentioning

confidence: 99%

ALMA: Machine learning breastfeeding chatbot

Achtaich¹,

Achtaich²,

Fagroud³

et al. 2023

Math. Model. Comput.

View full text Add to dashboard Cite

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

De Souza,

Viswanath,

Echterhoff

et al. 2024

JMIR AI

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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.

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

De Souza,

Viswanath,

Echterhoff

et al. 2023

Preprint

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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 a convolution neural network (CNN) in detecting healthy lactating breasts and six breastfeeding-related issues by only using red, green, and blue (RGB) images. Our goal is to assess the applicability of this algorithm as an auxiliary resource for LCs to manage their time more effectively, respond promptly to patient needs, and enhance the overall experience and care for breastfeeding mothers. METHODS We evaluate the potential for a classification model to detect breastfeeding-related conditions using 1,000 breast and nipple images gathered from online and physical educational resources. We used the CNN VGG-16 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 present an analysis of the classification challenges, identifying image traits that may confound the detection model. RESULTS The model achieves an area under the ROC curve (AUC) above 0.93 and 90% accuracy in the multi-class classification of specific breastfeeding-related conditions. 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.

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Assessment on Adoption Behavior of First-time Mothers on the Usage of Chatbots for Breastfeeding Consultation

Cited by 4 publications

References 27 publications

ALMA: Machine learning breastfeeding chatbot

ALMA: Machine learning breastfeeding chatbot

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

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

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