German RZ, Crompton AW, Gould FDH, Thexton AJ. Animal models for dysphagia studies: what have we learnt so Far. Dysphagia. 2017;32:73–7.

Article  PubMed  PubMed Central  Google Scholar 

Mayerl CJ, Steer KE, Chava AM, Bond LE, Edmonds CE, Gould FDH et al. Anatomical and physiological variation of the hyoid musculature during swallowing in infant pigs. J Exp Biol. 2021;224.

Mayerl CJ, Myrla AM, Gould FDH, Bond LE, Stricklen BM, German RZ. Swallow safety is determined by bolus volume during infant feeding in an animal model. Dysphagia. 2021;36:120–9.

Article  PubMed  Google Scholar 

Edmonds CE, German RZ, Bond LE, Mayerl CJ. Oropharyngeal capsaicin exposure improves infant feeding performance in an animal model of superior laryngeal nerve damage. J Neurophysiol. 2022;128:339–49.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Steele CM, Peladeau-Pigeon M, Nagy A, Waito AA. Measurement of pharyngeal residue from lateral view videofluoroscopic images. J Speech Lang Hear Res. 2020;63:1404–15.

Article  PubMed  PubMed Central  Google Scholar 

Waito AA, Tabor-Gray LC, Steele CM, Plowman EK. Reduced pharyngeal constriction is associated with impaired swallowing efficiency in amyotrophic lateral sclerosis (ALS). Neurogastroenterol Motil. 2018;30.

Plowman EK, Tabor-Gray L, Rosado KM, Vasilopoulos T, Robison R, Chapin JL, et al. Impact of expiratory strength training in amyotrophic lateral sclerosis: results of a randomized, sham-controlled trial. Muscle Nerve. 2019;59:40–6.

Article  PubMed  Google Scholar 

Leonard R, Kendall KA, McKenzie S. Structural displacements affecting pharyngeal constriction in nondysphagic elderly and nonelderly adults. Dysphagia. 2004;19:133–41.

Article  PubMed  Google Scholar 

Dharmarathna I, Miles A, Allen J. Predicting penetration–aspiration through quantitative swallow measures of children: a videofluoroscopic study. Eur Arch Otorhinolaryngol. 2021;278:1907–16.

Article  PubMed  Google Scholar 

Mayerl CJ, Myrla AM, Bond LE, Stricklen BM, German RZ, Gould FDH. Premature birth impacts bolus size and shape through nursing in infant pigs. Pediatr Res. 2020;87:656–61.

Article  PubMed  Google Scholar 

Mayerl CJ, Kaczmarek EB, Smith AE, Shideler HE, Blilie ME, Edmonds CE et al. A Ducted, Biomimetic Nipple Improves Aspects of Infant Feeding Physiology and Performance in an Animal Model. Dysphagia [Internet]. 2024 [cited 2024 Dec 11];1–10. Available from: https://link.springer.com/article/10.1007/s00455-024-10780-5

Steer KE, Johnson ML, Edmonds CE, Adjerid K, Bond LE, German RZ et al. The Impact of Varying Nipple Properties on Infant Feeding Physiology and Performance Throughout Ontogeny in a Validated Animal Model. Dysphagia [Internet]. 2024 [cited 2024 Dec 11];39:460–7. Available from: https://link.springer.com/article/10.1007/s00455-023-10630-w

Mayerl CJ, Edmonds CE, Gould FDH, German RZ. Increased viscosity of milk during infant feeding improves swallow safety through modifying sucking in an animal model. J Texture Stud [Internet]. 2021 [cited 2024 Dec 11];52:603–11. Available from: https://onlinelibrary.wiley.com/doi/full/https://doi.org/10.1111/jtxs.12599

Kawamura LR, de SM, Sarmet M, de Campos PS, Takehara S, Kumei Y, Zeredo JLL. Apnea behavior in early- and late-stage mouse models of Parkinson’s disease: Cineradiographic analysis of spontaneous breathing, acute stress, and swallowing. Respir Physiol Neurobiol [Internet]. 2024 [cited 2024 Dec 13];323. Available from: https://pubmed.ncbi.nlm.nih.gov/38395210/

Dharmarathna I, Miles A, Allen J. Quantifying bolus residue and its risks in children: A videofluoroscopic study. Am J Speech Lang Pathol. 2021;30:687–96.

Article  PubMed  Google Scholar 

Jauk S, Kramer D, Veeranki SPK, Siml-Fraissler A, Lenz-Waldbauer A, Tax E, et al. Evaluation of a machine Learning-Based dysphagia prediction tool in clinical routine: A prospective observational cohort study. Dysphagia. 2023;38:1238–46.

Article  PubMed  PubMed Central  Google Scholar 

Kim JK, Choo YJ, Choi GS, Shin H, Chang MC, Park D. Deep learning analysis to automatically detect the presence of penetration or aspiration in videofluoroscopic swallowing study. J Korean Med Sci. 2022;37.

Martin-Martinez A, Miró J, Amadó C, Ruz F, Ruiz A, Ortega O, et al. A systematic and universal artificial intelligence screening method for oropharyngeal dysphagia: improving diagnosis through risk management. Dysphagia. 2023;38:1224–37.

Article  PubMed  Google Scholar 

Sakai K, Gilmour S, Hoshino E, Nakayama E, Momosaki R, Sakata N et al. A machine learning-based screening test for sarcopenic dysphagia using image recognition. Nutrients. 2021;13.

Santoso LF, Baqai F, Gwozdz M, Lange J, Rosenberger MG, Sulzer J, et al. Applying machine learning algorithms for automatic detection of swallowing from sound. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:2584–8.

PubMed  Google Scholar 

Frakking TT, Chang AB, Carty C, Newing J, Weir KA, Schwerin B, et al. Using an automated speech recognition approach to differentiate between normal and aspirating swallowing sounds recorded from digital cervical auscultation in children. Dysphagia. 2022;37:1482–92.

Article  PubMed  PubMed Central  Google Scholar 

O’Brien MK, Botonis OK, Larkin E, Carpenter J, Martin-Harris B, Maronati R, et al. Advanced machine learning tools to monitor biomarkers of dysphagia: A wearable sensor Proof-of-Concept study. Digit Biomark. 2021;5:167–75.

Article  PubMed  PubMed Central  Google Scholar 

Ye F, Cheng L-L, Li W-M, Guo Y, Fan X-FA, Machine-Learning. Model Based on Clinical Features for the Prediction of Severe Dysphagia After Ischemic Stroke. Int J Gen Med [Internet]. 2024 [cited 2024 Dec 13];17:5623–31. Available from: https://pubmed.ncbi.nlm.nih.gov/39624613/

Shu K, Mao S, Zhang Z, Coyle JL, Sejdić E. Recent advancements and future directions in automatic swallowing analysis via videofluoroscopy: A review. Comput Methods Programs Biomed [Internet]. 2025 [cited 2024 Dec 13];259. Available from: https://pubmed.ncbi.nlm.nih.gov/39579458/

Kim JM, Kim MS, Choi SY, Lee K, Ryu JS. A deep learning approach to dysphagia-aspiration detecting algorithm through pre- and post-swallowing voice changes. Front Bioeng Biotechnol [Internet]. 2024 [cited 2024 Dec 13];12. Available from: https://pubmed.ncbi.nlm.nih.gov/39157445/

Shin B, Lee SH, Kwon K, Lee YJ, Crispe N, Ahn SY et al. Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics. Adv Sci (Weinh) [Internet]. 2024 [cited 2024 Dec 13];11. Available from: https://pubmed.ncbi.nlm.nih.gov/38981027/

Hsiao MY, Weng CH, Wang YC, Cheng SH, Wei KC, Tung PY, et al. Deep learning for automatic hyoid tracking in videofluoroscopic swallow studies. Dysphagia. 2023;38:171–80.

Article  PubMed  Google Scholar 

Donohue C, Mao S, Sejdić E, Coyle JL. Tracking hyoid bone displacement during swallowing without videofluoroscopy using machine learning of vibratory signals. Dysphagia. 2021;36:259–69.

Article  PubMed  Google Scholar 

Donohue C, Khalifa Y, Perera S, Sejdić E, Coyle JL. How closely do machine ratings of duration of UES opening during videofluoroscopy approximate clinician ratings using Temporal kinematic analyses and the MBSImP? Dysphagia. 2021;36:707–18.

Article  PubMed  Google Scholar 

Lee JT, Park E, Hwang JM, Jung T, Du, Park D. Machine learning analysis to automatically measure response time of pharyngeal swallowing reflex in videofluoroscopic swallowing study. Sci Rep. 2020;10.

Riebold B, Seidl RO, Schauer T. Electromyography- and Bioimpedance-Based Detection of Swallow Onset for the Control of Dysphagia Treatment. Sensors (Basel) [Internet]. 2024 [cited 2024 Dec 13];24. Available from: https://pubmed.ncbi.nlm.nih.gov/39460005/

Heo S, Uhm KE, Yuk D, Kwon BM, Yoo B, Kim J et al. Deep learning approach for dysphagia detection by syllable-based speech analysis with daily conversations. Sci Rep [Internet]. 2024 [cited 2024 Dec 13];14. Available from: https://pubmed.ncbi.nlm.nih.gov/39217249/

Ariji Y, Gotoh M, Fukuda M, Watanabe S, Nagao T, Katsumata A et al. A preliminary deep learning study on automatic segmentation of contrast-enhanced bolus in videofluorography of swallowing. Sci Rep. 2022;12.

Shaheen N, Burdick R, Peña-Chávez R, Ulmschneider C, Yee J, Kurosu A et al. Use of deep learning to segment bolus during videofluoroscopic swallow studies. Biomed Phys Eng Express. 2024;10.

Li W, Mao S, Mahoney AS, Petkovic S, Coyle JL, Sejdić E. Deep learning models for bolus segmentation in videofluoroscopic swallow studies. J Real Time Image Process [Internet]. 2024 [cited 2024 Dec 11];21:1–10. Available from: https://link.springer.com/article/https://doi.org/10.1007/s11554-023-01398-1

Oliveira AM, Coelho L, Carvalho E, Ferreira-Pinto MJ, Vaz R, Aguiar P. Machine learning for adaptive deep brain stimulation in Parkinson’s disease: closing the loop. J Neurol [Internet]. 2023 [cited 2024 Dec 13];270:5313–26. Available from: https://pubmed.ncbi.nlm.nih.gov/37530789/

Turgeon S, Lanovaz MJ, Tutorial. Applying machine learning in behavioral research. Perspect Behav Sci. 2020;43:697–723.

Article  PubMed  PubMed Central  Google Scholar 

Pareek A, Martin RK. Editorial commentary: machine learning in medicine requires clinician input, faces barriers, and High-Quality evidence is required to demonstrate improved patient outcomes. Arthrosc - J Arthroscopic Relat Surg. 2022;38:2106–8.

Article  Google Scholar 

Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, Haubold C, et al. Ilastik: interactive machine learning for (bio)image analysis. Nat Methods. 2019;16:1226–32.

Article  CAS  PubMed  Google Scholar 

Mayerl CJ, Edmonds CE, Catchpole EA, Myrla AM, Gould FDH, Bond LE, et al. Sucking versus swallowing coordination, integration, and performance in preterm and term infants. J Appl Physiol. 2020;129:1383–92.

Article  PubMed  PubMed Central  Google Scholar 

Brainerd EL, Baier DB, Gatesy SM, Hedrick TL, Metzger KA, Gilbert SL et al. X-ray reconstruction of moving morphology (XROMM): precision, accuracy and applications in comparative biomechanics research. J Exp Zool A Ecol Genet Physiol [Internet]. 2010 [cited 2024 Dec 11];313:262–79. Available from: https://pubmed.ncbi.nlm.nih.gov/20095029/

Gierbolini-Norat EM, Holman SD, Ding P, Bakshi S, German RZ. Variation in the timing and frequency of sucking and swallowing over an entire feeding session in the infant pig Sus scrofa. Dysphagia. 2014;29:475–82.

Article  PubMed  PubMed Central  Google Scholar 

McGrattan KE, McGhee HC, McKelvey KL, Clemmens CS, Hill EG, DeToma A, et al. Capturing infant swallow impairment on videofluoroscopy: timing matters. Pediatr Radiol. 2020;50:199–206.

Article  PubMed  Google Scholar 

Schneider CA, Rasband WS, Eliceiri KW. NIH image to imageJ: 25 years of image analysis. Nat Methods. 2012;9:671–5.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gould FDH, Mayerl CJ, Adjerid K, Edmonds C, Charles N, Johnson M, et al. Impact of volume and rate of milk delivery on coordination of respiration and swallowing in infant pigs. J Exp Zool Ecol Integr Physiol. 2023;339:1052–8.

Article  Google Scholar 

Bayona HHG, Inamoto Y, Saitoh E, Aihara K, Kobayashi M, Otaka Y. Prediction of Pharyngeal 3D Volume Using 2D Lateral Area Measurements During Swallowing. Dysphagia. 2024.

Haubold C, Schiegg M, Kreshuk A, Berg S, Koethe U, Hamprecht FA. Segmenting and tracking multiple dividing targets using Ilastik. Adv Anat Embryol Cell Biol. 2016;219:199–229.

Article  PubMed  Google Scholar 

Kreshuk A, Zhang C. Machine Learning: Advanced Image Segmentation Using ilastik. Methods in Molecular Biology [Internet]. 2019 [cited 2024 Dec 11];2040:449–63. Available from: https://link.springer.com/protocol/10.1007/978-1-4939-9686-5_21

Bayer D, Antonucci S, Müller HP, Saad R, Dupuis L, Rasche V et al. Disruption of orbitofrontal-hypothalamic projections in a murine ALS model and in human patients. Transl Neurodegener. 2021;10.