Development of AI Web Service for Diagnosis of Swallowing Disorders

doi:10.22716/sckt.2023.11.4.038

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2023 Vol.11, Issue 4 Preview Page Next Page

Research Article

Development of AI Web Service for Diagnosis of Swallowing Disorders 삼킴장애 진단을 위한 AI 웹 서비스 개발: Dong-Wook Lim¹, Chung-Sub Lee², Hee-Kyung Moon³
임동욱¹, 이충섭², 문희경³; ¹Researcher, Medical Convergence Research Center, Wonkwang University
²Senior Researcher, Office of Educational Innovation, Wonkwang University
³Research Professor, Office of Educational Innovation, Wonkwang University

¹원광대학교 의료융합연구센터 연구원
²원광대학교 의료융합연구센터 책임연구원
³원광대학교 교육혁신원 연구교수

31 December 2023. pp. 93-104

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Abstract

삼킴장애 진단의 대표적인 방법은 비디오 투시 연하검사(VFSS)로, 음식물이 입에서 식도로 이동하는 과정을 X-선으로 촬영한 동영상 데이터를 통해 평가한다. 임상환경에서 VFSS 검사는 보통 30분 정도 소요되지만 결과를 얻기 위해서는 상당한 대기시간이 필요하다. 본 연구에서는 삼킴장애 진단을 위한 AI 모델을 개발하고 이를 웹 서비스로 구현하였다. 이를 위해서 VFSS 동영상 데이터 수집과 학습 데이터 생성이 필요하였다. 학습데이터 생성에 필요한 데이터 저장 관리와 분류, 그리고 학습데이터 생성을 지원하는 시스템을 개발하였다. 이를 기반으로 삼킴장애 AI 모델은 실시간 객체 탐지에 가장 성능이 좋은 YOLOv7 모델을 채택하여 개발하였다. 특히 임상현장에서 얻어진 VFSS 데이터를 실시간으로 빠르게 결과를 확인할 수 있는 웹 기반 서비스 운영으로 적용하였다. 개발된 모델은 삼킴장애 진단에 효과적으로 활용될 수 있을 것으로 기대한다.

A representative method of diagnosing swallowing disorders is video fluoroscopic swallowing (VFSS), which evaluates the process of food moving from the mouth to the esophagus through video data captured by X-rays. In a clinical setting, a VFSS test usually takes about 30 minutes, but there is a significant waiting time to obtain results. In this study, we developed an AI model for diagnosing swallowing disorders and implemented it as a web service. For this purpose, it was necessary to collect VFSS video data and create learning data. To this end, we developed a system that supports data storage management and classification necessary for generating learning data, and generating learning data. Based on this, the swallowing disorder AI model was developed by adopting the YOLOv7 model, which has the best performance in real-time object detection. In particular, VFSS data obtained at clinical sites were applied to operate a web-based service that allows quick confirmation of results in real time. It is expected that the developed model can be effectively used in diagnosing swallowing disorders.

Keywords

VFSS

Diagnosis of swallowing disorders

Penetration

Aspiration

YOLOv7

References

P. E. Marik, and D. Kaplan, "Aspiration pneumonia and dysphagia in the elderly", Chest Journal, Vol. 124, No. 1, pp. 328-336, 2003. 10.1378/chest.124.1.328 12853541
Z. Zhang, S. Mao, J. Coyle, and E, Sejdić, "Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning", Medical Image Analysis, Vol. 74, pp. 102218, 2021. 10.1016/j.media.2021.102218 34487983 PMC8560570
B. Mokhlesi, J. A. Logemann, A. W. Rademaker, C. A. Stangl, and T. C. Corbridge, "Oropharyngeal deglutition in stable COPD ", Chest Journal, Vol. 121, No. 2, pp. 361-369, 2002. 10.1378/chest.121.2.361 11834644
P. E. Marik, "Aspiration pneumonitis and aspiration pneumonia", New England Journal of Medicine, Vol. 344, No. 9, pp. 665-671, 2001. 10.1056/NEJM200103013440908 11228282
Statistics Korea. Annual report on the cause of death statistics. 2020.
R. D. Stevenson, and J.H. Allaire, "The development of normal feeding and swallowing", Pediatric Clinics of North America, Vol. 38, No. 6, pp. 1439-1453, 1991. 10.1016/S0031-3955(16)38229-3 1945550
J. A. Logemann, "Evaluation and treatment of swallowing disorders", American Journal of Speech-Language Pathology. Vol. 3, No. 3, pp. 41-44, 1994. 10.1044/1058-0360.0303.41
M. Yoshida, Y. Miura, S. Okada, M. Yamada, H. Kagaya, E. Saitoh, Y. Kamakura, Y. Okawa, Y. Matsuyama, and H. Sanada, "Effectiveness of Swallowing Care on Safe Oral Intake Using Ultrasound-Based Observation of Residues in the Epiglottis Valley: A Pragmatic, Quasi-Expermen tal Study", Healthcare, Vol. 8, No.1, pp. 50, 2020. 10.3390/healthcare8010050 32120866 PMC7151232
G. L. Re, F. Vernuccio, M. L. Di Vittorio, L. Scopelliti, A. Di Piazza, M. C. Terranova, D. Picone, C. Tudisca, and S. Salerno, "Swallowing evaluation with videofluoroscopy in the paediatric population", Acta Otorhinolaryngol Ital, Vol. 39, No. 5, pp. 279-288, 2019. 10.14639/0392-100X-1942 30933173 PMC6843585
B. Martin-Harris, and B. Jones, "The videofluorographic swallowing study", Physical Medicine and Rehabilitation Clinics of North America, Vol. 19, No. 4, pp. 769-785, 2008. 10.1016/j.pmr.2008.06.004 18940640 PMC2586156
M. Tan, and Q. V. Le, "EfficientNetV2: Smaller Models and Faster Training", International Conference on Machine Learning PMLR, pp. 10096-10106, 2021.
C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464-7475, 2023. 10.1109/CVPR52729.2023.00721 38005185 PMC10673005
R. L. Galvez, A. A. Bandala, E. P. Dadios, R. R. P. Vicerra, and J. M. Z. Maningo, "Object Detection Using Convolutional Neural Networks", TENCON 2018 - 2018 IEEE Region 10 Conference, pp. 2023-2027, 2018. 10.1109/TENCON.2018.8650517
J. K. Kim, Y. J. Choo, G. S. Choi, H. Shin, M. C. Chang, and D. Park, "Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study", Journal of Korean Medical Science, Vol. 37, No. 6, pp. 1-8, 2022. 10.3346/jkms.2022.37.e42 35166079 PMC8845107
Y. Ariji, M. Gotoh, M. Fukuda, S. Watanabe, T. Nagao, A. Katsumata, and E. Ariji, "A preliminary deep learning study on automatic segmentation of contrast-enhanced bolus in videofluorography of swallowing", Scientific Reports, Vol. 12, No. 1, pp. 18754, 2022. 10.1038/s41598-022-21530-8 36335226 PMC9637105

Information

Publisher :The Society of Convergence Knowledge
Publisher(Ko) :융복합지식학회
Journal Title :The Society of Convergence Knowledge Transactions
Journal Title(Ko) :융복합지식학회논문지
Volume : 11
No :4
Pages :93-104
DOI :https://doi.org/10.22716/sckt.2023.11.4.038

[1] P. E. Marik, and D. Kaplan, "Aspiration pneumonia and dysphagia in the elderly", Chest Journal, Vol. 124, No. 1, pp. 328-336, 2003. 10.1378/chest.124.1.328 12853541

[2] Z. Zhang, S. Mao, J. Coyle, and E, Sejdić, "Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning", Medical Image Analysis, Vol. 74, pp. 102218, 2021. 10.1016/j.media.2021.102218 34487983 PMC8560570

[3] B. Mokhlesi, J. A. Logemann, A. W. Rademaker, C. A. Stangl, and T. C. Corbridge, "Oropharyngeal deglutition in stable COPD ", Chest Journal, Vol. 121, No. 2, pp. 361-369, 2002. 10.1378/chest.121.2.361 11834644

[4] P. E. Marik, "Aspiration pneumonitis and aspiration pneumonia", New England Journal of Medicine, Vol. 344, No. 9, pp. 665-671, 2001. 10.1056/NEJM200103013440908 11228282

[5] Statistics Korea. Annual report on the cause of death statistics. 2020.

[6] R. D. Stevenson, and J.H. Allaire, "The development of normal feeding and swallowing", Pediatric Clinics of North America, Vol. 38, No. 6, pp. 1439-1453, 1991. 10.1016/S0031-3955(16)38229-3 1945550

[7] J. A. Logemann, "Evaluation and treatment of swallowing disorders", American Journal of Speech-Language Pathology. Vol. 3, No. 3, pp. 41-44, 1994. 10.1044/1058-0360.0303.41

[8] M. Yoshida, Y. Miura, S. Okada, M. Yamada, H. Kagaya, E. Saitoh, Y. Kamakura, Y. Okawa, Y. Matsuyama, and H. Sanada, "Effectiveness of Swallowing Care on Safe Oral Intake Using Ultrasound-Based Observation of Residues in the Epiglottis Valley: A Pragmatic, Quasi-Expermen tal Study", Healthcare, Vol. 8, No.1, pp. 50, 2020. 10.3390/healthcare8010050 32120866 PMC7151232

[9] G. L. Re, F. Vernuccio, M. L. Di Vittorio, L. Scopelliti, A. Di Piazza, M. C. Terranova, D. Picone, C. Tudisca, and S. Salerno, "Swallowing evaluation with videofluoroscopy in the paediatric population", Acta Otorhinolaryngol Ital, Vol. 39, No. 5, pp. 279-288, 2019. 10.14639/0392-100X-1942 30933173 PMC6843585

[10] B. Martin-Harris, and B. Jones, "The videofluorographic swallowing study", Physical Medicine and Rehabilitation Clinics of North America, Vol. 19, No. 4, pp. 769-785, 2008. 10.1016/j.pmr.2008.06.004 18940640 PMC2586156

[11] M. Tan, and Q. V. Le, "EfficientNetV2: Smaller Models and Faster Training", International Conference on Machine Learning PMLR, pp. 10096-10106, 2021.

[12] C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464-7475, 2023. 10.1109/CVPR52729.2023.00721 38005185 PMC10673005

[13] R. L. Galvez, A. A. Bandala, E. P. Dadios, R. R. P. Vicerra, and J. M. Z. Maningo, "Object Detection Using Convolutional Neural Networks", TENCON 2018 - 2018 IEEE Region 10 Conference, pp. 2023-2027, 2018. 10.1109/TENCON.2018.8650517

[14] J. K. Kim, Y. J. Choo, G. S. Choi, H. Shin, M. C. Chang, and D. Park, "Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study", Journal of Korean Medical Science, Vol. 37, No. 6, pp. 1-8, 2022. 10.3346/jkms.2022.37.e42 35166079 PMC8845107

[15] Y. Ariji, M. Gotoh, M. Fukuda, S. Watanabe, T. Nagao, A. Katsumata, and E. Ariji, "A preliminary deep learning study on automatic segmentation of contrast-enhanced bolus in videofluorography of swallowing", Scientific Reports, Vol. 12, No. 1, pp. 18754, 2022. 10.1038/s41598-022-21530-8 36335226 PMC9637105

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