{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T23:54:41Z","timestamp":1788220481236,"version":"build-2803163510"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"am","delay-in-days":349,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["P01-DK117824"],"award-info":[{"award-number":["P01-DK117824"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R01-DK079902"],"award-info":[{"award-number":["R01-DK079902"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OAC 1450374"],"award-info":[{"award-number":["OAC 1450374"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OAC 1931372"],"award-info":[{"award-number":["OAC 1931372"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Artificial Intelligence in Medicine"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1016\/j.artmed.2022.102435","type":"journal-article","created":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T19:03:20Z","timestamp":1667243000000},"page":"102435","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":22,"special_numbering":"C","title":["Virtual disease landscape using mechanics-informed machine learning: Application to esophageal disorders"],"prefix":"10.1016","volume":"134","author":[{"given":"Sourav","family":"Halder","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Yamasaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shashank","family":"Acharya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjun","family":"Kou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guy","family":"Elisha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dustin A.","family":"Carlson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter J.","family":"Kahrilas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John E.","family":"Pandolfino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neelesh A.","family":"Patankar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.artmed.2022.102435_bb0005","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1111\/j.1365-2982.2004.00539.x","article-title":"High-resolution manometry predicts the success of oesophageal bolus transport and identifies clinically important abnormalities not detected by conventional manometry","volume":"16","author":"Fox","year":"2004","journal-title":"Neurogastroenterol Motil"},{"issue":"5","key":"10.1016\/j.artmed.2022.102435_bb0010","article-title":"High-resolution manometry of the EGJ: an analysis of crural diaphragm function in GERD","volume":"102","author":"Pandolfino","year":"2007","journal-title":"Off Journal of the American College of Gastroenterology"},{"issue":"3","key":"10.1016\/j.artmed.2022.102435_bb0015","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1136\/gut.2007.127993","article-title":"Oesophageal high-resolution manometry: moving from research into clinical practice","volume":"57","author":"Fox","year":"2008","journal-title":"Gut"},{"issue":"5","key":"10.1016\/j.artmed.2022.102435_bb0020","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1053\/j.gastro.2008.07.022","article-title":"Achalasia: a new clinically relevant classification by high-resolution manometry","volume":"135","author":"Pandolfino","year":"2008","journal-title":"Gastroenterology"},{"issue":"8","key":"10.1016\/j.artmed.2022.102435_bb0025","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1111\/j.1365-2982.2009.01311.x","article-title":"High-resolution manometry in clinical practice: utilizing pressure topography to classify oesophageal motility abnormalities","volume":"21","author":"Pandolfino","year":"2009","journal-title":"Neurogastroenterol Motil"},{"issue":"1","key":"10.1016\/j.artmed.2022.102435_bb0030","doi-asserted-by":"crossref","DOI":"10.1111\/nmo.14058","article-title":"Esophageal motility disorders on high-resolution manometry: Chicago classification version 4.0\u00a9","volume":"33","author":"Yadlapati","year":"2021","journal-title":"Neurogastroenterol Motil"},{"issue":"2","key":"10.1016\/j.artmed.2022.102435_bb0035","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1111\/nmo.12071","article-title":"Evaluation of esophageal motor function in clinical practice","volume":"25","author":"Gyawali","year":"2013","journal-title":"Neurogastroenterol Motil"},{"issue":"12","key":"10.1016\/j.artmed.2022.102435_bb0040","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1038\/ajg.2016.454","article-title":"Evaluation of esophageal motility utilizing the functional lumen imaging probe","volume":"111","author":"Carlson","year":"2016","journal-title":"Am J Gastroenterol"},{"issue":"7","key":"10.1016\/j.artmed.2022.102435_bb0045","doi-asserted-by":"crossref","first-page":"1742","DOI":"10.1053\/j.gastro.2015.08.005","article-title":"The functional lumen imaging probe detects esophageal contractility not observed with manometry in patients with achalasia","volume":"149","author":"Carlson","year":"2015","journal-title":"Gastroenterology"},{"issue":"10","key":"10.1016\/j.artmed.2022.102435_bb0050","doi-asserted-by":"crossref","DOI":"10.1111\/nmo.14116","article-title":"Evaluating esophageal motility beyond primary peristalsis: assessing esophagogastric junction opening mechanics and secondary peristalsis in patients with normal manometry","volume":"33","author":"Carlson","year":"2021","journal-title":"Neurogastroenterol Motil"},{"issue":"2","key":"10.1016\/j.artmed.2022.102435_bb0055","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/BF01307535","article-title":"Biomechanical characteristics of the human esophagus","volume":"38","author":"Orvar","year":"1993","journal-title":"Dig Dis Sci"},{"issue":"1","key":"10.1016\/j.artmed.2022.102435_bb0060","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1053\/j.gastro.2010.09.037","article-title":"Mechanical properties of the esophagus in eosinophilic esophagitis","volume":"140","author":"Kwiatek","year":"2011","journal-title":"Gastroenterology"},{"issue":"11","key":"10.1016\/j.artmed.2022.102435_bb0065","article-title":"Use of the functional lumen imaging probe in clinical esophagology","volume":"115","author":"Savarino","year":"2020","journal-title":"Off J Am Coll Gastroenterol"},{"issue":"4","key":"10.1016\/j.artmed.2022.102435_bb0070","doi-asserted-by":"crossref","DOI":"10.1097\/MOG.0000000000000272","article-title":"Functional lumen imaging probe: the FLIP side of esophageal disease","volume":"32","author":"Carlson","year":"2016","journal-title":"Curr Opin Gastroenterol"},{"issue":"12","key":"10.1016\/j.artmed.2022.102435_bb0075","doi-asserted-by":"crossref","first-page":"4547","DOI":"10.1007\/s00464-013-3121-2","article-title":"Comparison of esophagogastric junction distensibility changes during POEM and heller myotomy using intraoperative FLIP","volume":"27","author":"Teitelbaum","year":"2013","journal-title":"Surg Endosc"},{"issue":"6","key":"10.1016\/j.artmed.2022.102435_bb0080","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1007\/s00464-019-07028-x","article-title":"Intraoperative assessment of esophageal motility using FLIP during myotomy for achalasia","volume":"34","author":"Campagna","year":"2020","journal-title":"Surg Endosc"},{"issue":"1","key":"10.1016\/j.artmed.2022.102435_bb0085","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/1475-925X-3-40","article-title":"A two-layered mechanical model of the rat esophagus. Experiment and theory","volume":"3","author":"Fan","year":"2004","journal-title":"BioMed Eng OnLine"},{"issue":"9","key":"10.1016\/j.artmed.2022.102435_bb0090","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1016\/j.medengphy.2009.07.003","article-title":"Biomechanical behaviour of oesophageal tissues: material and structural configuration, experimental data and constitutive analysis","volume":"31","author":"Natali","year":"2009","journal-title":"Med Eng Phys"},{"issue":"9","key":"10.1016\/j.artmed.2022.102435_bb0095","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1016\/j.compbiomed.2013.06.009","article-title":"Structurally-motivated characterization of the passive pseudo-elastic response of esophagus and its layers","volume":"43","author":"Sokolis","year":"2013","journal-title":"Comput Biol Med"},{"issue":"16","key":"10.1016\/j.artmed.2022.102435_bb0100","doi-asserted-by":"crossref","first-page":"2654","DOI":"10.1016\/j.jbiomech.2009.08.018","article-title":"Biomechanical and histological characteristics of passive esophagus: experimental investigation and comparative constitutive modeling","volume":"42","author":"Stavropoulou","year":"2009","journal-title":"J Biomech"},{"issue":"3","key":"10.1016\/j.artmed.2022.102435_bb0105","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1115\/1.2187033","article-title":"Directional, regional, and layer variations of mechanical properties of esophageal tissue and its interpretation using a structure-based constitutive model","volume":"128","author":"Yang","year":"2005","journal-title":"Journal of Biomechanical Engineering"},{"issue":"6","key":"10.1016\/j.artmed.2022.102435_bb0110","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1115\/1.2354206","article-title":"3D mechanical properties of the layered esophagus: experiment and constitutive model","volume":"128","author":"Yang","year":"2006","journal-title":"J Biomech Eng"},{"issue":"7","key":"10.1016\/j.artmed.2022.102435_bb0115","doi-asserted-by":"crossref","DOI":"10.1115\/1.4050284","article-title":"Pumping patterns and work done during peristalsis in finite-length elastic tubes","volume":"143","author":"Acharya","year":"2021","journal-title":"J Biomech Eng"},{"issue":"1","key":"10.1016\/j.artmed.2022.102435_bb0120","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/BF02406976","article-title":"A fluid mechanical perspective on esophageal bolus transport","volume":"2","author":"Brasseur","year":"1987","journal-title":"Dysphagia"},{"issue":"1","key":"10.1016\/j.artmed.2022.102435_bb0125","doi-asserted-by":"crossref","first-page":"G21","DOI":"10.1152\/ajpgi.00235.2004","article-title":"The mechanical basis of impaired esophageal emptying postfundoplication","volume":"289","author":"Ghosh","year":"2005","journal-title":"Am J Physiol Gastrointest Liver Physiol"},{"issue":"3","key":"10.1016\/j.artmed.2022.102435_bb0130","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1007\/s10237-021-01420-0","article-title":"Mechanics informed fluoroscopy of esophageal transport","volume":"20","author":"Halder","year":"2021","journal-title":"Biomech Model Mechanobiol"},{"key":"10.1016\/j.artmed.2022.102435_bb0135","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.jcp.2015.05.049","article-title":"A fully resolved active musculo-mechanical model for esophageal transport","volume":"298","author":"Kou","year":"2015","journal-title":"J Comput Phys"},{"key":"10.1016\/j.artmed.2022.102435_bb0140","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.jcp.2017.07.025","article-title":"A continuum mechanics-based musculo-mechanical model for esophageal transport","volume":"348","author":"Kou","year":"2017","journal-title":"J Comput Phys"},{"key":"10.1016\/j.artmed.2022.102435_bb0145","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1017\/S0022112093000710","article-title":"Non-steady peristaltic transport in finite-length tubes","volume":"248","author":"Li","year":"1993","journal-title":"J Fluid Mech"},{"issue":"4","key":"10.1016\/j.artmed.2022.102435_bb0150","first-page":"G525","article-title":"Analyses of normal and abnormal esophageal transport using computer simulations. American journal of physiology-gastrointestinal and liver","volume":"266","author":"Li","year":"1994","journal-title":"Physiology"},{"issue":"9","key":"10.1016\/j.artmed.2022.102435_bb0155","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.3748\/wjg.v13.i9.1352","article-title":"Finite element simulation of food transport through the esophageal body","volume":"13","author":"Yang","year":"2007","journal-title":"World J Gastroenterol"},{"issue":"141","key":"10.1016\/j.artmed.2022.102435_bb0160","doi-asserted-by":"crossref","first-page":"20170387","DOI":"10.1098\/rsif.2017.0387","article-title":"Opportunities and obstacles for deep learning in biology and medicine","volume":"15","author":"Ching","year":"2018","journal-title":"J R Soc Interface"},{"issue":"1","key":"10.1016\/j.artmed.2022.102435_bb0165","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat Med"},{"key":"10.1016\/j.artmed.2022.102435_bb0170","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2018.04.005","article-title":"Deep learning for healthcare applications based on physiological signals: a review","volume":"161","author":"Faust","year":"2018","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.artmed.2022.102435_bb0175","doi-asserted-by":"crossref","first-page":"S36","DOI":"10.1016\/j.metabol.2017.01.011","article-title":"Artificial intelligence in medicine","volume":"69","author":"Hamet","year":"2017","journal-title":"Metabolism"},{"issue":"4","key":"10.1016\/j.artmed.2022.102435_bb0180","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc Neurol"},{"issue":"6","key":"10.1016\/j.artmed.2022.102435_bb0185","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1093\/bib\/bbx044","article-title":"Deep learning for healthcare: review, opportunities and challenges","volume":"19","author":"Miotto","year":"2018","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.artmed.2022.102435_bb0190","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.inffus.2020.09.006","article-title":"A survey on deep learning in medicine: why, how and when?","volume":"66","author":"Piccialli","year":"2021","journal-title":"Information Fusion"},{"issue":"14","key":"10.1016\/j.artmed.2022.102435_bb0195","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N Engl J Med"},{"issue":"1","key":"10.1016\/j.artmed.2022.102435_bb0200","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat Med"},{"issue":"10","key":"10.1016\/j.artmed.2022.102435_bb0205","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nature Biomedical Engineering"},{"issue":"1","key":"10.1016\/j.artmed.2022.102435_bb0210","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1053\/j.gastro.2019.08.058","article-title":"Application of artificial intelligence to gastroenterology and hepatology","volume":"158","author":"Le Berre","year":"2020","journal-title":"Gastroenterology"},{"issue":"4","key":"10.1016\/j.artmed.2022.102435_bb0215","doi-asserted-by":"crossref","first-page":"388","DOI":"10.5009\/gnl18384","article-title":"Overview of deep learning in gastrointestinal endoscopy","volume":"13","author":"Min","year":"2019","journal-title":"Gut and liver"},{"issue":"3","key":"10.1016\/j.artmed.2022.102435_bb0220","article-title":"Artificial intelligence-assisted gastroenterology\u2014 promises and Pitfalls","volume":"114","author":"Ruffle","year":"2019","journal-title":"Off Journal of the American College of Gastroenterology"},{"issue":"14","key":"10.1016\/j.artmed.2022.102435_bb0225","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":"10.1016\/j.artmed.2022.102435_bb0230","doi-asserted-by":"crossref","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":"10.1016\/j.artmed.2022.102435_bb0235","series-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2013"},{"issue":"5","key":"10.1016\/j.artmed.2022.102435_bb0240","doi-asserted-by":"crossref","first-page":"G780","DOI":"10.1152\/ajpgi.00032.2021","article-title":"Estimation of mechanical work done to open the esophagogastric junction using functional lumen imaging probe panometry","volume":"320","author":"Acharya","year":"2021","journal-title":"Am. J. Physiol. Gastrointest. Liver Physiol."},{"issue":"6","key":"10.1016\/j.artmed.2022.102435_bb0245","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/S0006-3495(66)86690-0","article-title":"A theory of fluid flow in compliant tubes","volume":"6","author":"Barnard","year":"1966","journal-title":"Biophys J"},{"issue":"1","key":"10.1016\/j.artmed.2022.102435_bb0250","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/S0022112079001348","article-title":"Unsteady flow in a collapsible tube subjected to external pressure or body forces","volume":"95","author":"Kamm","year":"1979","journal-title":"J Fluid Mech"},{"issue":"1","key":"10.1016\/j.artmed.2022.102435_bb0255","doi-asserted-by":"crossref","DOI":"10.1063\/1.2165780","article-title":"One-dimensional model of valveless pumping in a closed loop and a numerical solution","volume":"18","author":"Manopoulos","year":"2006","journal-title":"Phys Fluids"},{"issue":"4","key":"10.1016\/j.artmed.2022.102435_bb0260","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s00285-002-0179-1","article-title":"Valveless pumping in a fluid-filled closed elastic tube-system: one-dimensional theory with experimental validation","volume":"46","author":"Ottesen","year":"2003","journal-title":"J Math Biol"},{"issue":"2","key":"10.1016\/j.artmed.2022.102435_bb0265","doi-asserted-by":"crossref","DOI":"10.3390\/info11020125","article-title":"Albumentations: fast and flexible image augmentations","volume":"11","author":"Buslaev","year":"2020","journal-title":"Information"},{"key":"10.1016\/j.artmed.2022.102435_bb0270","series-title":"Adam: A Method for Stochastic Optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.artmed.2022.102435_bb0275","author":"Chollet"},{"key":"10.1016\/j.artmed.2022.102435_bb0280","author":"Mart\u00edn Abadi"},{"key":"10.1016\/j.artmed.2022.102435_bb0285","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Gramfort","year":"2011","journal-title":"Journal of Machine Learning Research"},{"issue":"3","key":"10.1016\/j.artmed.2022.102435_bb0290","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1159\/000343435","article-title":"Severe recurrent achalasia cardia responding to treatment of severe autoimmune acquired haemophilia","volume":"6","author":"Al-Jafar","year":"2012","journal-title":"Case Rep Gastroenterol"},{"issue":"12","key":"10.1016\/j.artmed.2022.102435_bb0295","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1016\/j.cgh.2011.08.002","article-title":"Achalasia with dense eosinophilic infiltrate responds to steroid therapy","volume":"9","author":"Savarino","year":"2011","journal-title":"Clin Gastroenterol Hepatol"},{"issue":"11","key":"10.1016\/j.artmed.2022.102435_bb0300","article-title":"Can eosinophilic esophagitis cause achalasia and other esophageal motility disorders?","volume":"113","author":"Spechler","year":"2018","journal-title":"Off Journal of the American College of Gastroenterology"},{"issue":"6","key":"10.1016\/j.artmed.2022.102435_bb0305","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1097\/00000658-199706000-00003","article-title":"Laparoscopic heller myotomy and fundoplication for achalasia","volume":"225","author":"Hunter","year":"1997","journal-title":"Ann Surg"},{"issue":"3","key":"10.1016\/j.artmed.2022.102435_bb0310","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1097\/01.sla.0000136940.32255.51","article-title":"Heller myotomy versus heller myotomy with dor fundoplication for achalasia: a prospective randomized double-blind clinical trial","volume":"240","author":"Richards","year":"2004","journal-title":"Ann Surg"},{"issue":"4","key":"10.1016\/j.artmed.2022.102435_bb0315","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1055\/s-0029-1244080","article-title":"Peroral endoscopic myotomy (POEM) for esophageal achalasia","volume":"42","author":"Inoue","year":"2010","journal-title":"Endoscopy"},{"issue":"2","key":"10.1016\/j.artmed.2022.102435_bb0320","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1053\/j.gastro.2013.04.057","article-title":"Peroral endoscopic myotomy for the treatment of achalasia: an international prospective multicenter study","volume":"145","author":"Von Renteln","year":"2013","journal-title":"Gastroenterology"},{"issue":"3","key":"10.1016\/j.artmed.2022.102435_bb0325","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.cgh.2016.10.022","article-title":"Functional lumen imaging probe for the Management of Esophageal Disorders: expert review from the clinical practice updates committee of the AGA Institute","volume":"15","author":"Hirano","year":"2017","journal-title":"Clin Gastroenterol Hepatol"},{"issue":"1","key":"10.1016\/j.artmed.2022.102435_bb0330","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1146\/annurev.fluid.36.050802.121944","article-title":"Experimental and computational methods in cardiovascular fluid mechanics","volume":"36","author":"Taylor","year":"2004","journal-title":"Annu Rev Fluid Mech"},{"issue":"1","key":"10.1016\/j.artmed.2022.102435_bb0335","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1146\/annurev.bioeng.3.1.421","article-title":"Respiratory fluid mechanics and transport processes","volume":"3","author":"Grotberg","year":"2001","journal-title":"Annu Rev Biomed Eng"},{"issue":"6","key":"10.1016\/j.artmed.2022.102435_bb0340","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1010143","article-title":"Pulmonary drug delivery and retention: a computational study to identify plausible parameters based on a coupled airway-mucus flow model","volume":"18","author":"Chakravarty","year":"2022","journal-title":"PLoS Comput Biol"}],"container-title":["Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0933365722001877?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0933365722001877?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T15:15:59Z","timestamp":1760541359000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0933365722001877"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":68,"alternative-id":["S0933365722001877"],"URL":"https:\/\/doi.org\/10.1016\/j.artmed.2022.102435","relation":{},"ISSN":["0933-3657"],"issn-type":[{"value":"0933-3657","type":"print"}],"subject":[],"published":{"date-parts":[[2022,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Virtual disease landscape using mechanics-informed machine learning: Application to esophageal disorders","name":"articletitle","label":"Article Title"},{"value":"Artificial Intelligence in Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.artmed.2022.102435","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"102435"}}