{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:52:27Z","timestamp":1785423147544,"version":"3.56.0"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T00:00:00Z","timestamp":1738886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Ministry of Health &amp; Welfare, Republic of Korea","doi-asserted-by":"publisher","award":["HI23C1283"],"award-info":[{"award-number":["HI23C1283"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Ministry of Health &amp; Welfare, Republic of Korea","doi-asserted-by":"publisher","award":["RS-2023-00279315"],"award-info":[{"award-number":["RS-2023-00279315"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT)","doi-asserted-by":"publisher","award":["HI23C1283"],"award-info":[{"award-number":["HI23C1283"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT)","doi-asserted-by":"publisher","award":["RS-2023-00279315"],"award-info":[{"award-number":["RS-2023-00279315"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Functional dyspepsia (FD) is a common functional gastrointestinal disorder characterized by chronic digestive symptoms without identifiable structural abnormalities. FD affects approximately 8\u201346% of the population, leading to significant socioeconomic burdens due to reduced quality of life and productivity. Traditional medicine utilizes differential diagnosis through comprehensive examinations, which include observing and questioning, abdominal examination, and pulse diagnosis for functional gastrointestinal disorders. However, challenges persist in the standardization and objectivity of diagnostic protocols. Methods: This study aims to develop an artificial intelligence-based algorithm to predict identified patterns in patients with functional dyspepsia by integrating brain\u2013body bio-signals, including brain activity measured by functional near-infrared spectroscopy, pulse wave, skin conductance response, and electrocardiography. We will conduct an observational cross-sectional study comprising 100 patients diagnosed according to the Rome IV criteria, collecting bio-signal data alongside differential diagnoses performed by licensed Korean medicine doctors. The study protocol was reviewed and approved by the Institutional Review Board of Kyung Hee University Hospital at Gangdong on 25 January 2024 (IRB no. KHNMCOH 2023-12-003-003) and was registered in the Korean Clinical Trial Registry (KCT0009275). Results: By creating AI algorithms based on bio-signals and integrating them into clinical practice, the objectivity and reliability of traditional diagnostics are expected to be enhanced. Conclusions: The integration of bio-signal analysis into the diagnostic process for patients with FD will improve clinical practices and support the broader acceptance of traditional-medicine diagnostic processes in healthcare.<\/jats:p>","DOI":"10.3390\/jcm14041072","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T03:39:47Z","timestamp":1739158787000},"page":"1072","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Pattern Identification in Patients with Functional Dyspepsia Using Brain\u2013Body Bio-Signals: Protocol of a Clinical Trial for AI Algorithm Development"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5883-5401","authenticated-orcid":false,"given":"Won-Joon","family":"Koh","sequence":"first","affiliation":[{"name":"Department of Korean Medicine, Graduate School, Kyung Hee University, Seoul 02453, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7252-3420","authenticated-orcid":false,"given":"Junsuk","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Information Convergence, Kwangwoon University, Seoul 01897, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6787-2215","authenticated-orcid":false,"given":"Younbyoung","family":"Chae","sequence":"additional","affiliation":[{"name":"Department of Meridians and Acupoints, College of Korean Medicine, Kyung Hee University, Seoul 02453, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9275-3111","authenticated-orcid":false,"given":"In-Seon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Meridians and Acupoints, College of Korean Medicine, Kyung Hee University, Seoul 02453, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9902-0938","authenticated-orcid":false,"given":"Seok-Jae","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Digestive Diseases, College of Korean Medicine, Kyung Hee University, Seoul 02453, Republic of Korea"},{"name":"Department of Korean Internal Medicine, College of Korean Medicine, Kyung Hee University, Kyung Hee University Hospital at Gangdong, Seoul 05278, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.jep.2012.01.033","article-title":"Syndrome differentiation in modern research of traditional Chinese medicine","volume":"140","author":"Jiang","year":"2012","journal-title":"J. Ethnopharmacol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ha, N.-Y., Ko, S.-J., Park, J.-W., and Kim, J. (2024). Development of a Standard Tool of Pattern Identification for Functional Dyspepsia: A Cross-Sectional Study from Korea. Healthcare, 12.","DOI":"10.3390\/healthcare12232331"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1089\/acm.2016.0056","article-title":"Reliability of Manual Pulse Diagnosis Methods in Traditional East Asian Medicine: A Systematic Narrative Literature Review","volume":"22","author":"Bilton","year":"2016","journal-title":"J. Altern. Complement. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1089\/acm.2019.0197","article-title":"Experimental Studies of Inter-Rater Agreement in Traditional Chinese Medicine: A Systematic Review","volume":"25","author":"Jacobson","year":"2019","journal-title":"J. Altern. Complement. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e2109357","DOI":"10.1002\/adma.202109357","article-title":"Wearable Pressure Sensors for Pulse Wave Monitoring","volume":"34","author":"Meng","year":"2022","journal-title":"Adv. Mater."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.bja.2020.09.049","article-title":"Cardiac output estimation using pulse wave analysis-physiology, algorithms, and technologies: A narrative review","volume":"126","author":"Saugel","year":"2021","journal-title":"Br. J. Anaesth."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tago, K., Wang, H., and Jin, Q. (2019, January 9\u201313). Classification of TCM pulse diagnoses based on pulse and periodic features from personal health data. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9014237"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Guo, C., Jiang, Z., He, H., Liao, Y., and Zhang, D. (2022). Wrist pulse signal acquisition and analysis for disease diagnosis: A review. Comput. Biol. Med., 143.","DOI":"10.1016\/j.compbiomed.2022.105312"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1053\/j.gastro.2016.03.035","article-title":"Rome IV-Functional GI Disorders: Disorders of Gut-Brain Interaction","volume":"150","author":"Drossman","year":"2016","journal-title":"Gastroenterology"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chae, Y., and Lee, I.S. (2023). Central Regulation of Eating Behaviors in Humans: Evidence from Functional Neuroimaging Studies. Nutrients, 15.","DOI":"10.3390\/nu15133010"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1007\/s00535-023-02031-5","article-title":"Brain activity in response to food images in patients with irritable bowel syndrome and functional dyspepsia","volume":"58","author":"Katsumata","year":"2023","journal-title":"J. Gastroenterol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1093\/ajcn\/nqy077","article-title":"Fat label compared with fat content: Gastrointestinal symptoms and brain activity in functional dyspepsia patients and healthy controls","volume":"108","author":"Lee","year":"2018","journal-title":"Am. J. Clin. Nutr."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ali, M.K., and Chen, J.D.Z. (2023). Roles of Heart Rate Variability in Assessing Autonomic Nervous System in Functional Gastrointestinal Disorders: A Systematic Review. Diagnostics, 13.","DOI":"10.3390\/diagnostics13020293"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1007\/BF01296514","article-title":"Gastric and autonomic responses to stress in functional dyspepsia","volume":"31","author":"Camilleri","year":"1986","journal-title":"Dig. Dis. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1097\/MCG.0000000000001802","article-title":"The Overlap Subgroup of Functional Dyspepsia Exhibits More Severely Impaired Gastric and Autonomic Functions","volume":"58","author":"Cheng","year":"2024","journal-title":"J. Clin. Gastroenterol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1097\/j.pain.0000000000001417","article-title":"Machine learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics","volume":"160","author":"Lee","year":"2019","journal-title":"Pain"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.clinph.2024.07.018","article-title":"An approach to the detection of pain from autonomic and cortical correlates","volume":"166","author":"Chouchou","year":"2024","journal-title":"Clin. Neurophysiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.jpain.2022.08.010","article-title":"Optimizing and Accelerating the Development of Precision Pain Treatments for Chronic Pain: IMMPACT Review and Recommendations","volume":"24","author":"Edwards","year":"2023","journal-title":"J. Pain"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s13167-022-00271-8","article-title":"Predicting acupuncture efcacy for functional dyspepsia based on routine clinical features: A machine learning study in the framework of predictive, preventive, and personalized medicine","volume":"13","author":"Yin","year":"2022","journal-title":"EPMA J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3511","DOI":"10.1093\/cercor\/bhac288","article-title":"Predicting acupuncture efficacy for functional dyspepsia based on functional brain network features: A machine learning study","volume":"33","author":"Yin","year":"2023","journal-title":"Cereb.Cortex"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101115","DOI":"10.1016\/j.imr.2024.101115","article-title":"Discovering the key symptoms for identifying patterns in functional dyspepsia patients: Doctor\u2019s decision and machine learning","volume":"14","author":"Yoon","year":"2025","journal-title":"Integr. Med. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.jocm.2018.07.002","article-title":"Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis","volume":"28","author":"Alwosheel","year":"2018","journal-title":"J. Choice Model."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101861","DOI":"10.1016\/j.artmed.2020.101861","article-title":"Deep neural network for semi-automatic classification of term and preterm uterine recordings","volume":"105","author":"Chen","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hao, Z., Zhai, X., Cheng, D., Pan, Y., and Dou, W. (2022). EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics. Front. Neurosci., 16.","DOI":"10.3389\/fnins.2022.848737"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dimitriadis, S.I., and Salis, C.I. (2017). Mining Time-Resolved Functional Brain Graphs to an EEG-Based Chronnectomic Brain Aged Index (CBAI). Front. Hum. Neurosci., 11.","DOI":"10.3389\/fnhum.2017.00423"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"116","DOI":"10.15188\/kjopp.2016.04.30.2.116","article-title":"Development on the Questionnaire of Cold-Heat Pattern Identification Based on Usual Symptoms for Health Promotion\u2014Focused on Reliability Study","volume":"30","author":"Yeo","year":"2016","journal-title":"J. Physiol. Pathol. Korean Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"171","DOI":"10.15188\/kjopp.2018.06.32.3.171","article-title":"Evaluation of Reliability and Validity for Deficiency and Excess Pattern Identification Questionnaire","volume":"32","author":"Jang","year":"2018","journal-title":"J. Physiol. Pathol. Korean Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"142","DOI":"10.15188\/kjopp.2020.06.34.3.142","article-title":"Evaluation of Validity for Deficiency and Excess Pattern Identification Questionnaire","volume":"34","author":"Baek","year":"2020","journal-title":"J. Physiol. Pathol. Korean Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"563","DOI":"10.22246\/jikm.2020.41.4.563","article-title":"Development of a Phlegm Pattern Questionnaire for Functional Dyspepsia and Analysis of Its Reliability and Validity","volume":"41","author":"Baek","year":"2020","journal-title":"J. Int. Korean Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"390","DOI":"10.22246\/jikm.2019.40.3.390","article-title":"Development of a Food Retention Questionnaire for Functional Dyspepsia and the Analysis of Its Reliability and Validity","volume":"40","author":"Hwang","year":"2019","journal-title":"J. Int. Korean Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"157","DOI":"10.13048\/jkm.14015","article-title":"Study on the Development of a standard instrument of diagnosis and assessment for spleen deficiency pattern","volume":"35","author":"Oh","year":"2014","journal-title":"J. Korean Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"23","DOI":"10.13048\/jkm.15017","article-title":"Reliability and Validity Analysis of a Standard Instrument of Diagnosis and Assessment for Spleen Qi Deficiency Pattern in Chronic Dyspepsia Patients","volume":"36","author":"Kim","year":"2015","journal-title":"J. Korean Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.eujim.2017.11.009","article-title":"Development and validation of a new pattern identification scale for Stomach Qi Deficiency","volume":"17","author":"Lee","year":"2018","journal-title":"Eur. J. Integr. Med."},{"key":"ref_34","first-page":"48S","article-title":"Validation of the Nepean dyspepsia index-Korean version","volume":"9","author":"Lee","year":"2003","journal-title":"Korean J. Neurogastroenterol. Mot."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.2044-8260.1992.tb00997.x","article-title":"The development of a six-item short-form of the state scale of the Spielberger State\u2014Trait Anxiety Inventory (STAI)","volume":"31","author":"Marteau","year":"1992","journal-title":"Br. J. Clin. Psychol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Beck, A.T., Steer, R.A., and Brown, G. (1996). Beck depression inventory\u2013II. APA PsycTests.","DOI":"10.1037\/t00742-000"},{"key":"ref_37","first-page":"155","article-title":"EuroQol-5D (EQ-5D): An instrument for measuring quality of life. Monaldi Arch","volume":"78","author":"Balestroni","year":"2012","journal-title":"Chest Dis."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.3758\/s13428-020-01516-y","article-title":"NeuroKit2: A Python toolbox for neurophysiological signal processing","volume":"53","author":"Makowski","year":"2021","journal-title":"Behav. Res. Methods"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A real-time QRS detection algorithm","volume":"3","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","article-title":"Complex network measures of brain connectivity: Uses and interpretations","volume":"52","author":"Rubinov","year":"2010","journal-title":"Neuroimage"},{"key":"ref_42","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_43","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cohen, M. (2014). Analyzing Neural Time Series Data: Theory and Practice, MIT Press.","DOI":"10.7551\/mitpress\/9609.001.0001"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ins.2017.04.012","article-title":"Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network","volume":"405","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ancillon, L., Elgendi, M., and Menon, C. (2022). Machine Learning for Anxiety Detection Using Biosignals: A Review. Diagnostics, 12.","DOI":"10.3390\/diagnostics12081794"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sajino, E., Bartolotta, S., Tuena, C., Cipresso, P., Pedroli, E., and Riva, G. (2023). Machine learning in biosignals processing for mental health: A narrative review. Front. Psychol., 13.","DOI":"10.3389\/fpsyg.2022.1066317"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/4\/1072\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:29:04Z","timestamp":1760027344000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/4\/1072"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,7]]},"references-count":47,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["jcm14041072"],"URL":"https:\/\/doi.org\/10.3390\/jcm14041072","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,7]]}}}