{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:01:46Z","timestamp":1775325706661,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Social Fund financing","award":["56437\/24.07.2019."],"award-info":[{"award-number":["56437\/24.07.2019."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>High-resolution esophageal manometry is used for the study of esophageal motility disorders, with the help of catheters with up to 36 sensors. Color pressure topography plots are generated and analyzed and using the Chicago algorithm a final diagnosis is established. One of the main parameters in this algorithm is integrated relaxation pressure (IRP). The procedure is time consuming. Our aim was to firstly develop a machine learning based solution to detect probe positioning failure and to create a classifier to automatically determine whether the IRP is in the normal range or higher than the cut-off, based solely on the raw images. The first step was the preprocessing of the images, by finding the region of interest\u2014the exact moment of swallowing. Afterwards, the images were resized and rescaled, so they could be used as input for deep learning models. We used the InceptionV3 deep learning model to classify the images as correct or failure in catheter positioning and to determine the exact class of the IRP. The accuracy of the trained convolutional neural networks was above 90% for both problems. This work is just the first step in fully automating the Chicago Classification, reducing human intervention.<\/jats:p>","DOI":"10.3390\/s22010253","type":"journal-article","created":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T23:29:07Z","timestamp":1640906947000},"page":"253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal Manometry Using Machine Learning"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7497-0251","authenticated-orcid":false,"given":"Zoltan","family":"Czako","sequence":"first","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, 400027 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teodora","family":"Surdea-Blaga","sequence":"additional","affiliation":[{"name":"Second Medical Department, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400027 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gheorghe","family":"Sebestyen","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, 400027 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4116-694X","authenticated-orcid":false,"given":"Anca","family":"Hangan","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, 400027 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan Lucian","family":"Dumitrascu","sequence":"additional","affiliation":[{"name":"Second Medical Department, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400027 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liliana","family":"David","sequence":"additional","affiliation":[{"name":"Second Medical Department, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400027 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giuseppe","family":"Chiarioni","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, University of Verona, AOUI Verona, 37134 Verona, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3187-2894","authenticated-orcid":false,"given":"Edoardo","family":"Savarino","sequence":"additional","affiliation":[{"name":"Gastroenterology Unit, Department of Surgery, Oncology and Gastroenterology, University of Padua, 35100 Padova, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5508-2598","authenticated-orcid":false,"given":"Stefan Lucian","family":"Popa","sequence":"additional","affiliation":[{"name":"Second Medical Department, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400027 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1097\/MOG.0000000000000369","article-title":"High-resolution esophageal manometry: Interpretation in clinical practice","volume":"33","author":"Yadlapati","year":"2017","journal-title":"Curr. Opin. Gastroenterol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/dote\/dox068","article-title":"Trends in diagnoses after implementation of the Chicago classification for esophageal motility disorders (V3.0) for high-resolution manometry studies","volume":"30","author":"Laing","year":"2017","journal-title":"Dis. Esophagus"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1111\/nmo.12477","article-title":"The Chicago Classification of esophageal motility disorders, v3.0","volume":"27","author":"Kahrilas","year":"2015","journal-title":"Neurogastroenterol. Motil."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1038\/ajg.2017.10","article-title":"The Chicago Classification 3.0 Results in More Normal Findings and Fewer Hypotensive Findings with No Difference in Other Diagnoses","volume":"112","author":"Monrroy","year":"2017","journal-title":"Am. J. Gastroenterol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"506","DOI":"10.5056\/jnm24031","article-title":"Factors Determining the Inter-observer Variability and Diagnostic Accuracy of High-resolution Manometry for Esophageal Motility Disorders","volume":"24","author":"Kim","year":"2018","journal-title":"J. Neurogastroenterol. Motil."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1111\/dote.12278","article-title":"Inter-observer agreement for diagnostic classification of esophageal motility disorders defined in high-resolution manometry","volume":"28","author":"Fox","year":"2015","journal-title":"Dis. Esophagus"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.knosys.2015.01.010","article-title":"Transfer learning using computational intelligence: A survey","volume":"80","author":"Lu","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"ref_8","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (1997, January 17\u201319). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Juan, PR, USA."},{"key":"ref_9","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Processing Syst."},{"key":"ref_10","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network in Network. arXiv."},{"key":"ref_11","unstructured":"Diederik, P.K., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.3748\/wjg.v25.i14.1666","article-title":"Application of artificial intelligence in gastroenterology","volume":"25","author":"Yang","year":"2019","journal-title":"World J. Gastroenterol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6191","DOI":"10.3748\/wjg.v27.i37.6191","article-title":"Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology","volume":"27","author":"Christou","year":"2021","journal-title":"World J. Gastroenterol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1053\/j.gastro.2018.04.003","article-title":"Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience","volume":"154","author":"Misawa","year":"2018","journal-title":"Gastroenterology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1016\/j.cgh.2017.12.022","article-title":"Machine Learning in Detection of Undiagnosed Celiac Disease","volume":"16","author":"Hujoel","year":"2018","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1016\/j.cgh.2009.08.011","article-title":"Predicting early mortality after acute varicealhemorrhage based on classification and regression tree analysis","volume":"7","author":"Augustin","year":"2009","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1097\/01.meg.0000243885.55562.7e","article-title":"Prediction of significant fibrosis in hepatitis C virus infected liver transplant recipients by artificial neural network analysis of clinical factors","volume":"18","author":"Piscaglia","year":"2006","journal-title":"Eur. J. Gastroenterol. Hepatol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1053\/j.gastro.2016.04.004","article-title":"Characterization of Colorectal Lesions Using a Computer-Aided Diagnostic System for Narrow-Band Imaging Endocytoscopy","volume":"150","author":"Misawa","year":"2016","journal-title":"Gastroenterology"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s00535-020-01743-2","article-title":"Artificial intelligence automates and augments baseline impedance measurements from pH-impedance studies in gastroesophageal reflux disease","volume":"56","author":"Rogers","year":"2021","journal-title":"J. Gastroenterol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1002\/lary.23655","article-title":"Artificial neural network classification of pharyngeal high-resolution manometry with impedance data","volume":"123","author":"Hoffman","year":"2013","journal-title":"Laryngoscope"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1044\/1092-4388(2011\/11-0088)","article-title":"Application of classification models to pharyngeal high-resolution manometry","volume":"55","author":"Mielens","year":"2012","journal-title":"J. Speech Lang. Hear. Res."},{"key":"ref_22","first-page":"473","article-title":"High-resolution manometry: Reliability of automated analysis of upper esophageal sphincter relaxation parameters","volume":"25","author":"Lee","year":"2014","journal-title":"Turk. J. Gastroenterol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.physbeh.2016.08.005","article-title":"Calculation of upper esophageal sphincter restitution time from high resolution manometry data using machine learning","volume":"165","author":"Jungheim","year":"2016","journal-title":"Physiol. Behav."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1002\/lary.23987","article-title":"Three-dimensional analysis of pharyngeal high-resolution manometry data","volume":"123","author":"Geng","year":"2013","journal-title":"Laryngoscope"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1109\/TBME.2017.2758441","article-title":"A Procedure for the Automatic Analysis of High-Resolution Manometry Data to Support the Clinical Diagnosis of Esophageal Motility Disorders","volume":"65","author":"Frigo","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"102006","DOI":"10.1016\/j.artmed.2020.102006","article-title":"A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder","volume":"112","author":"Kou","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1159\/000511931","article-title":"How to Cope with Big Data in Functional Analysis of the Esophagus","volume":"36","author":"Jell","year":"2020","journal-title":"Visc. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/253\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:56:01Z","timestamp":1760169361000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,30]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010253"],"URL":"https:\/\/doi.org\/10.3390\/s22010253","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,30]]}}}