Infants Sucking Pattern Identification Using Machine-Learned Computational ModelingSource: Journal of Engineering and Science in Medical Diagnostics and Therapy:;2024:;volume( 008 ):;issue: 003::page 31003-1Author:Olapojoye, Abdullahi
,
Singh, Abhishek
,
Nishi, Eri
,
Fei, Baowei
,
Nostratinia, Aria
,
Hassanipour, Fatemeh
DOI: 10.1115/1.4066459Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Breastfeeding involves a complex coordination of swallowing, breathing, and sucking, with the infant's sucking proficiency being crucial for adequate nutrient intake. However, real-time assessment of milk intake is difficult, and issues with sucking often become apparent after the infant shows signs of nutrient deficiency. Traditional assessments by clinicians rely on the expertise and subjective judgment of healthcare professionals, enabling personalized evaluations. In this research, we introduce a novel approach to identifying sucking patterns by leveraging data collected from infants during breastfeeding sessions. This method utilizes artificial nipple-based sensors to capture the tongue forces exerted by infants, generating valuable clinical data. In the analysis of the collected time-series data, we applied machine-learned computational modeling (MLCM) algorithms to extract pertinent features and identify distinctive sucking patterns. The best-performing model demonstrated an accuracy of 90%, an 80% recall score, a perfect 100% precision score, a 0.90 f1-score, and an area under the curve (AUC) of 0.80. The proposed classification system has the potential to serve as a reliable decision-support tool for clinicians, offering valuable insights into infants' sucking behaviors. By integrating machine learning (ML)-based computational modeling into clinical practice, we aim to enhance the early identification of unhealthy sucking patterns, allowing for timely interventions and pro-active healthcare management.
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contributor author | Olapojoye, Abdullahi | |
contributor author | Singh, Abhishek | |
contributor author | Nishi, Eri | |
contributor author | Fei, Baowei | |
contributor author | Nostratinia, Aria | |
contributor author | Hassanipour, Fatemeh | |
date accessioned | 2025-04-21T10:14:25Z | |
date available | 2025-04-21T10:14:25Z | |
date copyright | 9/30/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 2572-7958 | |
identifier other | jesmdt_008_03_031003.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4305776 | |
description abstract | Breastfeeding involves a complex coordination of swallowing, breathing, and sucking, with the infant's sucking proficiency being crucial for adequate nutrient intake. However, real-time assessment of milk intake is difficult, and issues with sucking often become apparent after the infant shows signs of nutrient deficiency. Traditional assessments by clinicians rely on the expertise and subjective judgment of healthcare professionals, enabling personalized evaluations. In this research, we introduce a novel approach to identifying sucking patterns by leveraging data collected from infants during breastfeeding sessions. This method utilizes artificial nipple-based sensors to capture the tongue forces exerted by infants, generating valuable clinical data. In the analysis of the collected time-series data, we applied machine-learned computational modeling (MLCM) algorithms to extract pertinent features and identify distinctive sucking patterns. The best-performing model demonstrated an accuracy of 90%, an 80% recall score, a perfect 100% precision score, a 0.90 f1-score, and an area under the curve (AUC) of 0.80. The proposed classification system has the potential to serve as a reliable decision-support tool for clinicians, offering valuable insights into infants' sucking behaviors. By integrating machine learning (ML)-based computational modeling into clinical practice, we aim to enhance the early identification of unhealthy sucking patterns, allowing for timely interventions and pro-active healthcare management. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Infants Sucking Pattern Identification Using Machine-Learned Computational Modeling | |
type | Journal Paper | |
journal volume | 8 | |
journal issue | 3 | |
journal title | Journal of Engineering and Science in Medical Diagnostics and Therapy | |
identifier doi | 10.1115/1.4066459 | |
journal fristpage | 31003-1 | |
journal lastpage | 31003-8 | |
page | 8 | |
tree | Journal of Engineering and Science in Medical Diagnostics and Therapy:;2024:;volume( 008 ):;issue: 003 | |
contenttype | Fulltext |