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Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study

Zi-Tong Chen, Xiao-Long Li, Feng-Shan Jin, Yi-Lei Shi, Lei Zhang, Hao-Hao Yin, Yu-Li Zhu, Xin-Yi Tang, Xi-Yuan Lin, Bei-Lei Lu, Qun Wang, Li-Ping Sun, Xiao-Xiang Zhu, Li Qiu, Hui-Xiong Xu, Le-Hang Guo

J Med Internet Res 2025;27:e70545

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Process Re-Engineering and Data Integration Using Fast Healthcare Interoperability Resources for the Multidisciplinary Treatment of Lung Cancer

Ching-Hsiung Lin, Bing-Yen Wang, Sheng-Hao Lin, Pei Hsuan Shih, Chin-Jing Lee, Yung Ting Huang, Shih Chieh Chen, Mei-Lien Pan

JMIR Cancer 2025;11:e53887

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Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study

Byungeun Shon, Sook Jin Seong, Eun Jung Choi, Mi-Ri Gwon, Hae Won Lee, Jaechan Park, Ho-Young Chung, Sungmoon Jeong, Young-Ran Yoon

JMIR AI 2025;4:e64845

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Effectiveness and Mechanisms of a Digital Mindfulness–Based Intervention for Subthreshold to Clinical Insomnia Symptoms in Pregnant Women: Randomized Controlled Trial

In this study, only 46% (37/80) of the participants completed at least 3 modules and were considered as adherent, which is close to the 52.4% and 56.76% adherence rate (based on the same criteria) reported in studies by Sun et al [34] and Felder et al [42], respectively, about the effectiveness of digital MBIs on perinatal depressive symptoms.

Juan Wang, Qiuhong Yang, Naixue Cui, Liuliu Wu, Xuan Zhang, Yaoyao Sun, Yongqi Huang, Fenglin Cao