Detection of sarcopenia using deep learning-based artificial intelligence body part measure system (AIBMS)

Abstract

Background: Sarcopenia is an aging syndrome that increases the risks of various adverse outcomes, including falls, fractures, physical disability, and death. Sarcopenia can be diagnosed through medical images-based body part analysis, which requires laborious and time-consuming outlining of irregular contours of abdominal body parts. Therefore, it is critical to develop an efficient computational method for automatically segmenting body parts and predicting diseases.Methods: In this study, we designed an Artificial Intelligence Body Part Measure System (AIBMS) based on deep learning to automate body parts segmentation from abdominal CT scans and quantification of body part areas and volumes. The system was developed using three network models, including SEG-NET, U-NET, and Attention U-NET, and trained on abdominal CT plain scan data.Results: This segmentation model was evaluated using multi-device developmental and independent test datasets and demonstrated a high level of accuracy with over 0.9 DSC score in segment body parts. Based on the characteristics of the three network models, we gave recommendations for the appropriate model selection in various clinical scenarios. We constructed a sarcopenia classification model based on cutoff values (Auto SMI model), which demonstrated high accuracy in predicting sarcopenia with an AUC of 0.874. We used Youden index to optimize the Auto SMI model and found a better threshold of 40.69.Conclusion: We developed an AI system to segment body parts in abdominal CT images and constructed a model based on cutoff value to achieve the prediction of sarcopenia with high accuracy.

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

“…Instead, we manually extract time-frequency domain features and investigate their importance in our classification task. Compared to deep learning models for sarcopenia classification, our methodology would have lower accuracy performance in general [ 61 ]. To improve, more potentially sensitive features such as complexity, orderliness, and core shape modeling could be further investigated and utilized in ML.…”

Metaheuristic-Based Feature Selection Methods for Diagnosing Sarcopenia with Machine Learning Algorithms

“…Initially, we excluded unreliable features such as unique IDs and items with limited responses (five or fewer). Given sarcopenia’s multifaceted nature and elusive etiology [ 2 , 3 , 4 ], we let algorithms consider comprehensive coverage across the KLoSA survey categories while selecting features. This approach aimed to uncover novel factors influencing sarcopenia diagnosis and shed light on the interplay between multiple factors and sarcopenia.…”

“…Consequently, addressing sarcopenia has become imperative as we navigate an era dominated by aging demographics. However, despite its significance, the precise mechanisms underlying sarcopenia remain elusive, confounding attempts to pinpoint causative factors [ 1 , 2 , 3 ].…”

“…Efforts to tackle this challenge have led to studies employing machine learning methodologies in sarcopenia diagnosis, offering novel insights beyond human capacity. Some methodologies integrate diverse datasets, such as microarrays, surveys, or imaging data, to discern crucial patient features for diagnosis [ 3 , 4 , 5 ]. For instance, Kang et al [ 5 ] utilized a random forest algorithm to select features based on various clinical criteria.…”

Deep-learning model for predicting physical fitness in possible sarcopenia: analysis of the Korean physical fitness award from 2010 to 2023

“…Furthermore, a previous study developed a deep-learning-based sarcopenia prediction model (wide and deep) using clinical laboratory markers ( 22 ), which demonstrated high accuracy (area under curve [AUC] score), as compared with that of ML model prediction methods (support vector regression, random forest regression, and extreme gradient boosting). Additionally, deep learning applications in healthcare are rapidly evolving ( 23–25 ), with significant advancements in sarcopenia classification models using computed tomography (CT) ( 23 ). Several studies have used ML models to predict sarcopenia using laboratory markers and muscle mass measurements or images, without incorporating physical fitness variables ( 24 , 26 ).…”