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AI in evaluating ambulation of stroke patients: severity classification with video and functional ambulation category scale.
Kim, Jeong-Hyun; Hong, Hyeon; Lee, Kyuwon; Jeong, Yeji; Ryu, Hokyoung; Kim, Hyundo; Jang, Seong-Ho; Park, Hyeng-Kyu; Han, Jae-Young; Park, Hye Jung; Bae, Hasuk; Oh, Byung-Mo; Kim, Won-Seok; Lee, Sang Yoon; Lee, Shi-Uk.
Afiliación
  • Kim JH; Department of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.
  • Hong H; Department of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.
  • Lee K; Department of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.
  • Jeong Y; Department of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.
  • Ryu H; Department of Graduate School of Technology and Innovation Management, Hanyang University, Seoul, South Korea.
  • Kim H; Department of Intelligence Computing, Hanyang University, Seoul, South Korea.
  • Jang SH; Department of Rehabilitation Medicine, Hanyang University, Guri Hospital, Gyeonggi-do, South Korea.
  • Park HK; Department of Physical & Rehabilitation Medicine, Regional Cardiocerebrovascular Center, Center for Aging and Geriatrics, Chonnam National University Medical School & Hospital, Gwangju, South Korea.
  • Han JY; Department of Physical & Rehabilitation Medicine, Regional Cardiocerebrovascular Center, Center for Aging and Geriatrics, Chonnam National University Medical School & Hospital, Gwangju, South Korea.
  • Park HJ; Department of Rehabilitation Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
  • Bae H; Department of Rehabilitation Medicine, Ewha Woman's University, Seoul, South Korea.
  • Oh BM; Department of Rehabilitation, Seoul National University Hospital, Seoul, South Korea.
  • Kim WS; Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, South Korea.
  • Lee SY; Department of Rehabilitation Medicine, Seoul National University College of Medicine, SMG-SNU Boramae Medical Center, Seoul, South Korea.
  • Lee SU; Department of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.
Top Stroke Rehabil ; : 1-9, 2024 Jun 06.
Article en En | MEDLINE | ID: mdl-38841903
ABSTRACT

BACKGROUND:

The evaluation of gait function and severity classification of stroke patients are important to determine the rehabilitation goal and the level of exercise. Physicians often qualitatively evaluate patients' walking ability through visual gait analysis using naked eye, video images, or standardized assessment tools. Gait evaluation through observation relies on the doctor's empirical judgment, potentially introducing subjective opinions. Therefore, conducting research to establish a basis for more objective judgment is crucial.

OBJECTIVE:

To verify a deep learning model that classifies gait image data of stroke patients according to Functional Ambulation Category (FAC) scale.

METHODS:

Gait vision data from 203 stroke patients and 182 healthy individuals recruited from six medical institutions were collected to train a deep learning model for classifying gait severity in stroke patients. The recorded videos were processed using OpenPose. The dataset was randomly split into 80% for training and 20% for testing.

RESULTS:

The deep learning model attained a training accuracy of 0.981 and test accuracy of 0.903. Area Under the Curve(AUC) values of 0.93, 0.95, and 0.96 for discriminating among the mild, moderate, and severe stroke groups, respectively.

CONCLUSION:

This confirms the potential of utilizing human posture estimation based on vision data not only to develop gait parameter models but also to develop models to classify severity according to the FAC criteria used by physicians. To develop an AI-based severity classification model, a large amount and variety of data is necessary and data collected in non-standardized real environments, not in laboratories, can also be used meaningfully.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Top Stroke Rehabil Asunto de la revista: ANGIOLOGIA / REABILITACAO Año: 2024 Tipo del documento: Article País de afiliación: Corea del Sur Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Top Stroke Rehabil Asunto de la revista: ANGIOLOGIA / REABILITACAO Año: 2024 Tipo del documento: Article País de afiliación: Corea del Sur Pais de publicación: Reino Unido