Hunter CJ, Tulunay-Ugur OE (2024) Dysphagia in the aging population. Otolaryngol Clin North Am 57(4):685–693

Article  PubMed  Google Scholar 

Smith R, Bryant L, Hemsley B (2023) The true cost of dysphagia on quality of life: The views of adults with swallowing disability. Int J Language Commun Disorders 58(2):451–466

Article  Google Scholar 

Allen J, Greene M, Sabido I, Stretton M, Miles A (2020) Economic costs of dysphagia among hospitalized patients. Laryngoscope 130(4):974–979

Article  PubMed  Google Scholar 

Sheehan NJ (2008) Dysphagia and other manifestations of oesophageal involvement in the musculoskeletal diseases. Rheumatology 47(6):746–752

Article  CAS  PubMed  Google Scholar 

Wilkinson JM, Halland M (2020) Esophageal motility disorders. Am Fam Phys 102(5):291–296

Google Scholar 

Bredenoord AJ, Smout AJPM (2008) High-resolution manometry. Dig Liver Dis 40(3):174–181

Article  CAS  PubMed  Google Scholar 

Carlson DA, Pandolfino JE (2015) High-resolution manometry in clinical practice. Gastroenterol Hepatol 11(6):374

Google Scholar 

Fox MR, Pandolfino JE, Sweis R, Sauter M, Abreu Y Abreu AT, Anggiansah A, Bogte A, Bredenoord AJ, Dengler W, Elvevi A, Fruehauf H, Gellersen S, Ghosh S, Gyawali CP, Heinrich H, Hemmink M, Jafari J, Kaufman E, Kessing K, Kwiatek M, Lubomyr B, Banasiuk M, Mion F, Pérez-de-la-Serna J, Remes-Troche JM, Rohof W, Roman S, Ruiz-de-León A, Tutuian R, Uscinowicz M, Valdovinos MA, Vardar R, Velosa M, Waśko-Czopnik D, Weijenborg P, Wilshire C, Wright J, Zerbib F, Menne D (2015) Inter-observer agreement for diagnostic classification of esophageal motility disorders defined in high-resolution manometry. Dis Esophagus Off J Int Soc Dis Esophagus 28(8):874845

Google Scholar 

Kim JH, Kim SE, Cho YK, Lim CH, Park MI, Hwang JW, Jang JS, Oh M (2018) Factors determining the inter-observer variability and diagnostic accuracy of high-resolution manometry for esophageal motility disorders. J Neurogastroenterol Motility 24(1):58

Article  Google Scholar 

Gong EJ, Bang CS, Lee JJ, Baik GH (2025) AI in esophageal motility disorders: systematic review of high-resolution manometry studies. J Med Internet Res 27(1):e85223

Article  PubMed  PubMed Central  Google Scholar 

Jungheim M, Busche A, Miller S, Schilling N, Schmidt-Thieme L, Ptok M (2016) Calculation of upper esophageal sphincter restitution time from high resolution manometry data using machine learning. Physiol Behavior 165:413–424

Article  CAS  Google Scholar 

Lee TH, Lee JS, Hong SJ, Lee JS, Jeon SR, Kim WJ, Kim HG, Cho JY, Kim JO, Cho JH, Park WY, Park JW, Lee YG (2014) High-resolution manometry: reliability of automated analysis of upper esophageal sphincter relaxation parameters. Turkish J Gastroenterol Off J Turkish Soc Gastroenterol 25(5):473–480

Article  Google Scholar 

Jell A, Kuttler C, Ostler D, Hüser N (2020) How to cope with big data in functional analysis of the esophagus. Visceral Med 36(6):439–442

Article  Google Scholar 

Geiger A, Wagner L, Rueckert D, Wilhelm D, Jell A (2025) A deep learning-based approach to enhance accuracy and feasibility of long-term high-resolution manometry examinations. Commun Med 5(1)

Geiger A, Bernhard L, Gassert F, Feußner H, Wilhelm D, Friess H, Jell A (2025) Towards multimodal visualization of esophageal motility: fusion of manometry, impedance, and videofluoroscopic image sequences. Int J Comput Assist Radiol Surg 20(4):713–721

Article  PubMed  Google Scholar 

Hoffman MR, Mielens JD, Omari TI, Rommel N, Jiang JJ, McCulloch TM (2013) Artificial neural network classification of pharyngeal high-resolution manometry with impedance data. Laryngoscope 123(3):713–720

Article  PubMed  Google Scholar 

Hoffman MR, Jones CA, Geng Z, Abelhalim SM, Walczak CC, Mitchell AR, Jiang JJ, McCulloch TM (2013) Classification of high-resolution manometry data according to videofluoroscopic parameters using pattern recognition. Otolaryngol Head Neck Surg 149(1):454554

Article  Google Scholar 

Carniel EL, Frigo A, Costantini M, Giuliani T, Nicoletti L, Merigliano S, Natali AN (2016) A physiological model for the investigation of esophageal motility in healthy and pathologic conditions. In Proceedings of the institution of mechanical engineers (Part H: Journal of Engineering in Medicine) 230(9)

Frigo A, Costantini M, Fontanella CG, Salvador R, Merigliano S, Carniel EL (2018) A procedure for the automatic analysis of high-resolution manometry data to support the clinical diagnosis of esophageal motility disorders. IEEE Trans Biomed Eng 65(7):1476–1485

Article  PubMed  Google Scholar 

Zifan A, Lin J, Peng Z, Bo Y, Mittal RK (2023) Unraveling functional dysphagia: a game-changing automated machine-learning diagnostic approach. Appl Sci 13(18):10116

Article  CAS  Google Scholar 

Zifan A, Lee JM, Mittal RK (2024) Enhancing the diagnostic yield of esophageal manometry using distension-contraction plots of peristalsis and artificial intelligence. Am J Physiol Gastrointestinal Liver Physiol 327(3):G405–G413

Popa SL, Surdea-Blaga T, Dumitrascu DL, Chiarioni G, Savarino E, David L, Ismaiel A, Leucuta DC, Zsigmond I, Sebestyen G, Hangan A, Czako Z (2022) Automatic diagnosis of high-resolution esophageal manometry using artificial intelligence. J Gastrointestin Liver Dis 31(4):383–389

Article  PubMed  Google Scholar 

Surdea-Blaga T, Sebestyen G, Czako Z, Hangan A, Dumitrascu DL, Ismaiel A, David L, Zsigmond I, Chiarioni G, Savarino E, Leucuta DC, Popa SL (2022) Automated Chicago classification for esophageal motility disorder diagnosis using machine learning. Sensors 22(14):5227

Article  PubMed  PubMed Central  Google Scholar 

Kou W, Galal GO, Klug MW, Mukhin V, Carlson DA, Etemadi M, Kahrilas PJ, Pandolfino JE (2022) Deep learning based artificial intelligence model for identifying swallow types in esophageal high-resolution manometry. Neurogastroenterol Motility Off J Eur Gastrointestinal Motil Soc 34(7):5227

Google Scholar 

Kou W, Carlson DA, Baumann AJ, Donnan EN, Schauer JM, Etemadi M, Pandolfino JE (2022) A multi-stage machine learning model for diagnosis of esophageal manometry. Artif Intell Med 124:102233

Article  PubMed  Google Scholar 

Wang Z, Hou M, Yan L, Dai Y, Yin Y, Liu X (2021) Deep learning for tracing esophageal motility function over time. Comput Methods Prog Biomed 207:106212

Article  Google Scholar 

Rafieivand S, Moradi MH, Momayez Sanat Z, Asl Soleimani H (2023) A fuzzy-based framework for diagnosing esophageal mobility disorder using high-resolution manometry. J Biomed Inf 141:104355

Article  Google Scholar 

Wu X, Guo C, Lin J, Lin Z, Chen Q (2025) Mixed attention ensemble for esophageal motility disorders classification. PLoS ONE 20(2):e0317912

Article  CAS  PubMed  PubMed Central  Google Scholar 

Shim YK, Kim N, Park YH, Lee J-C, Sung J, Choi YJ, Yoon H, Shin CM, Park YS, Lee DH (2017) Effects of age on esophageal motility: use of high-resolution esophageal impedance manometry. J Neurogastroenterol Motil 23(2):229

Kunen LCB, Fontes LHS, Moraes-Filho JP, Assirati FS, Navarro-Rodriguez T (2020) Esophageal motility patterns are altered in older adult patients. Rev Gastroenterol Mex 85(3):264–274

CAS  Google Scholar 

Dantas RO, Ferriolli E, Souza MAN (1998) Gender effects on esophageal motility. Braz J Med Biol Res 31:539–544

Article  CAS  PubMed  Google Scholar 

Kamal A, Shakya S, Lopez R, Thota PN (2018) Gender, medication use and other factors associated with esophageal motility disorders in non-obstructive dysphagia. Gastroenterol Rep 6(3):177–183

Article  Google Scholar 

Takahashi S, Matsumura T, Kaneko T, Tokunaga M, Oura H, Ishikawa T, Nagashima A, Shiratori W, Akizue N, Ohta Y, Kikuchi A, Fujie M, Saito K, Okimoto K, Maruoka D, Nakagawa T, Arai M, Kato J, Kato N (2021) Clinical characteristics of esophageal motility disorders in patients with heartburn. J Neurogastroenterol Motility 27(4):545

Article  Google Scholar 

Le KHN, Low EE, Sharma P, Greytak M, Yadlapati R (2024) Normative high resolution esophageal manometry values in asymptomatic patients with obesity. Neurogastroenterol Motility 36(11):e14914

Article  Google Scholar 

Cohen DL, Hijazi B, Omari A, Bermont A, Shirin H, Said Ahmad H, Azzam N, Shibli F, Dickman R, Mari A (2023) Ethnic differences in clinical presentations and esophageal high-resolution manometry findings in patients with achalasia. Dysphagia 38(4):1247–1253

Article  PubMed  Google Scholar 

Kahrilas PJ, Bredenoord AJ, Fox M, Gyawali CP, Roman S, Smout AJPM, Pandolfino JE, Group IHRMW (2015) The Chicago classification of esophageal motility disorders, v3.0. Neurogastroenterol Motility 27(2):1247-1253

Qwen team: Qwen3 technical report (2025). https://arxiv.org/abs/2505.09388

Brody S, Alon U, Yahav E (2022) How attentive are graph attention networks? In: International conference on learning representations

Li G, Xiong C, Qian G, Thabet A, Ghanem B (2023) DeeperGCN: training deeper GCNs with generalized aggregation functions. IEEE Trans Pattern Anal Mach Intell 45(11):13024–13034

PubMed  Google Scholar 

Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D (2020) Supervised Contrastive Learning. Advances in Neural Information Processing Systems, vol 33

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Las Vegas

Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16x16 words: transformers for image recognition at scale. In: International conference on learning representations

Liu Z, Hu H, Lin Y, Yao Z, Xie Z, Wei Y, Ning J, Cao Y, Zhang Z, Dong L, Wei F, Guo B (2022) Swin Transformer V2: scaling up capacity and resolution. In: 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR)