Artificial intelligence in the diagnosis and management of dysphagia: a scoping review
Rayane Délcia da Silva; Suzanne Bettega Almeida; Flávio Magno Gonçalves; Bianca Simone Zeigelboim; José Stechman-Neto; Angela Graciela Deliga Schroder; Weslania Viviane Nascimento; Rosane Sampaio Santos; Cristiano Miranda de Araujo
Abstract
Keywords
Referencias
1 Baijens LW, Clave P, Cras P, Ekberg O, Forster A, Kolb GF, et al. European Society for Swallowing Disorders - European Union Geriatric Medicine Society white paper: oropharyngeal dysphagia as a geriatric syndrome. Clin Interv Aging. 2016;11:1403-28.
2 Zhao H, Jiang Y, Wang S, He F, Ren F, Zhang Z, et al. Dysphagia diagnosis system with integrated speech analysis from throat vibration. Expert Syst Appl. 2022;204:117496.
3 Cuadros-Acosta J, Orozco-Duque A. Automatic detection of poor quality signals as a pre-processing scheme in the analysis of sEMG in swallowing. Biomed Signal Process Control. 2022;71:103122.
4 Martin-Harris B, Canon CL, Bonilha HS, Murray J, Davidson K, Lefton-Greif MA. Best practices in modified barium swallow studies. Am J Speech Lang Pathol. 2020;29(2S):1078-93.
5 Sejdić E, Khalifa Y, Mahoney AS, Coyle JL. Artificial intelligence and dysphagia: novel solutions to old problems. Arq Gastroenterol. 2020;57(4):343-6.
6 Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 2021;13(1):152.
7 Shimizu H, Nakayama KI. Artificial intelligence in oncology. Cancer Sci. 2020;111(5):1452-60.
8 Lee SJ. Application of artificial intelligence in the area of dysphagia. J Rehabil Med. 2019;51(12):1-9.
9 Aromataris E, Munn Z, Porritt K, Pilla B, Jordan Z. JBI manual for evidence synthesis. Adelaide: JBI; 2024.
10 Open Science Framework [Internet]. 2024 [cited 2024 Sep 28]. Available from:
11 Haddaway NR, Grainger MJ, Gray CT. Citationchaser: a tool for transparent and efficient forward and backward citation chasing in systematic searching. Res Synth Methods. 2022;13(4):533-45.
12 Rayyan [Internet]. 2024 [cited 2024 Sep 28]. Available from:
13 Hill JE, Harris C, Clegg A. Methods for using Bing’s AI-powered search engine for data extraction for a systematic review. Res Synth Methods. 2024;15(2):347-53.
14 Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
15 Chang MW, Lin E, Hwang JN. Contour tracking using a knowledge-based snake algorithm to construct three-dimensional pharyngeal bolus movement. Dysphagia. 1999;14(4):219-27.
16 Park D, Son SI, Kim MS, Kim TY, Choi JH, Lee SE, et al. Machine learning predictive model for aspiration screening in hospitalized patients with acute stroke. Sci Rep. 2023;13(1):7835.
17 Zhang D, Chen Z, Xiao L, Zhu B, Wu R, Ou C, et al. Stretchable and durable HD-sEMG electrodes for accurate recognition of swallowing activities on complex epidermal surfaces. Microsyst Nanoeng. 2023;9(1):115.
18 Lai DK-H, Cheng ES-W, So BP-H, Mao Y-J, Cheung SM-Y, Cheung DSK, et al. Transformer models and convolutional networks with different activation functions for swallow classification using depth video data. Mathematics. 2023;11(14):3081.
19 Shaheen N, Burdick R, Pena-Chavez R, Ulmschneider C, Yee J, Kurosu A, et al. Use of deep learning to segment bolus during videofluoroscopic swallow studies. Biomed Phys Eng Express. 2023;10(1):1. PMid:37948874.
20 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(6):1482-92.
21 Mao S, Zhang Z, Khalifa Y, Donohue C, Coyle JL, Sejdic E. Neck sensor-supported hyoid bone movement tracking during swallowing. R Soc Open Sci. 2019;6(7):181982.
22 Lee D, Lee WH, Seo HG, Oh B-M, Lee JC, Kim HC. Online learning for the hyoid bone tracking during swallowing with neck movement adjustment using semantic segmentation. IEEE Access. 2020;8:157451-61.
23 Lee SJ, Ko JY, Kim HI, Choi S-I. Automatic detection of airway invasion from videofluoroscopy via deep learning technology. Appl Sci. 2020;10(18):6179.
24 Lee JT, Park E. Detection of the pharyngeal phase in the videofluoroscopic swallowing study using inflated 3D convolutional networks. In: Shi Y, Suk HI, Liu M, editors. Machine learning in medical imaging. Cham: Springer; 2018. p. 328-36. (Lecture Notes in Computer Science).
25 Donohue C, Khalifa Y, Mao S, Perera S, Sejdic E, Coyle JL. Establishing reference values for temporal kinematic swallow events across the lifespan in healthy community dwelling adults using high-resolution cervical auscultation. Dysphagia. 2022;37(3):664-75.
26 Coyle JL, Sejdic E. High-resolution cervical auscultation and data science: new tools to address an old problem. Am J Speech Lang Pathol. 2020;29(2S):992-1000.
27 Kritas S, Dejaeger E, Tack J, Omari T, Rommel N. Objective prediction of pharyngeal swallow dysfunction in dysphagia through artificial neural network modeling. Neurogastroenterol Motil. 2016;28(3):336-44.
28 Donohue C, Khalifa Y, Perera S, Sejdic E, Coyle JL. A preliminary investigation of whether HRCA signals can differentiate between swallows from healthy people and swallows from people with neurodegenerative diseases. Dysphagia. 2021;36(4):635-43.
29 Hoffman MR, Mielens JD, Omari TI, Rommel N, Jiang JJ, McCulloch TM. Artificial neural network classification of pharyngeal high-resolution manometry with impedance data. Laryngoscope. 2013;123(3):713-20.
30 Hoffman MR, Jones CA, Geng Z, Abelhalim SM, Walczak CC, Mitchell AR, et al. Classification of high-resolution manometry data according to videofluoroscopic parameters using pattern recognition. Otolaryngol Head Neck Surg. 2013;149(1):126-33.
31 Jones CA, Hoffman MR, Lin L, Abdelhalim S, Jiang JJ, McCulloch TM. Identification of swallowing disorders in early and mid-stage Parkinson’s disease using pattern recognition of pharyngeal high-resolution manometry data. Neurogastroenterol Motil. 2018;30(4):e13236.
32 Enz VCQ, Vaz ARC, Nunes MCA, Rosa MO, Nunes JA, Marques JM, et al. Accuracy of acoustic evaluation of swallowing as a diagnostic method of dysphagia in individuals affected by stroke: preliminary analysis. Dysphagia. 2022;37(4):724-35.
33 Weng W, Imaizumi M, Murono S, Zhu X. Expert-level aspiration and penetration detection during flexible endoscopic evaluation of swallowing with artificial intelligence-assisted diagnosis. Sci Rep. 2022;12(1):21689.
34 Lee KS, Lee E, Choi B, Pyun SB. Automatic pharyngeal phase recognition in untrimmed videofluoroscopic swallowing study using transfer learning with deep convolutional neural networks. Diagnostics. 2021;11(2):300.
35 Miyagi S, Sugiyama S, Kozawa K, Moritani S, Sakamoto SI, Sakai O. Classifying dysphagic swallowing sounds with support vector machines. Healthcare. 2020;8(2):103.
36 Khalifa Y, Coyle JL, Sejdic E. Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings. Sci Rep. 2020;10(1):8704.
37 Merey C, Kushki A, Sejdić E, Berall G, Chau T. Quantitative classification of pediatric swallowing through accelerometry. J Neuroeng Rehabil. 2012;9(1):34.
38 Kim Y, Kim HI, Park GS, Kim SY, Choi SI, Lee SJ. Reliability of machine and human examiners for detection of laryngeal penetration or aspiration in videofluoroscopic swallowing studies. J Clin Med. 2021;10(12):2681.
39 Inoue K, Yoshioka M, Yagi N, Nagami S, Oku Y. Using machine learning and a combination of respiratory flow, laryngeal motion, and swallowing sounds to classify safe and unsafe swallowing. IEEE Trans Biomed Eng. 2018;65(11):2529-41.
40 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(2):167-75.
41 Sabry A, Mahoney AS, Mao S, Khalifa Y, Sejdic E, Coyle JL. Automatic estimation of laryngeal vestibule closure duration using high- resolution cervical auscultation signals. Perspect ASHA Spec Interest Groups. 2020;5(6):1647-56.
42 Basiri B, Vali M, Agah S. Classification of normal and dysphagia in patients with GERD using swallowing sound analysis. In: 2017 Artificial Intelligence and Signal Processing Conference (AISP); 2017; Shiraz, Iran. Proceedings. New York: IEEE; 2017. p. 268-74.
43 Suzuki K, Shimizu Y, Ohshimo S, Oue K, Saeki N, Sadamori T, et al. Real-time assessment of swallowing sound using an electronic stethoscope and an artificial intelligence system. Clin Exp Dent Res. 2022;8(1):225-30.
44 Wang S, Jiang Y, Zhao H, Yang X, Zhang Z, Zhu C, et al. Smart dysphagia detection system with adaptive boosting analysis of throat signals. In: 2021 IEEE International Symposium on Circuits and Systems (ISCAS); 2021; Daegu, Korea. Proceedings. New York: IEEE; 2021. p. 1-5.
45 Lee J, Steele CM, Chau T. Swallow segmentation with artificial neural networks and multi-sensor fusion. Med Eng Phys. 2009;31(9):1049-55.
46 Khalifa Y, Donohue C, Coyle JL, Sejdic E. Upper esophageal sphincter opening segmentation with convolutional recurrent neural networks in high resolution cervical auscultation. IEEE J Biomed Health Inform. 2021;25(2):493-503.
47 Freed M, Burns B, Heller A, Sanchez D, Beaumont-Bowman S. A prototype intelligent assistant to help dysphagia patients eat safely at home. In: IJCAI’16: Twenty-Fifth International Joint Conference on Artificial Intelligence; 2016; New York USA. Proceedings.New York: ACM; 2016.
48 Lee WH, Lim MH, Seo HG, Seong MY, Oh BM, Kim S. Development of a novel prognostic model to predict 6-month swallowing recovery after ischemic stroke. Stroke. 2020;51(2):440-8.
49 Roldan-Vasco S, Orozco-Duque A, Suarez-Escudero JC, Orozco-Arroyave JR. Machine learning based analysis of speech dimensions in functional oropharyngeal dysphagia. Comput Methods Programs Biomed. 2021;208:106248.
50 Lee JC, Seo HG, Lee WH, Kim HC, Han TR, Oh BM. Computer-assisted detection of swallowing difficulty. Comput Methods Programs Biomed. 2016;134:79-88.
51 Lee HH, Kwon BM, Yang CK, Yeh CY, Lee J. Measurement of laryngeal elevation by automated segmentation using Mask R-CNN. Medicine. 2021;100(51):e28112.
52 Surdea-Blaga T, Sebestyen G, Czako Z, Hangan A, Dumitrascu DL, Ismaiel A, et al. Automated Chicago Classification for esophageal motility disorder diagnosis using machine learning. Sensors. 2022;22(14):5227.
53 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. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2019; Berlin, Germany. Proceedings. New York: IEEE; 2019. p. 2584-8.
54 Lizana García M. Automatic demarcation for videofluoroscopy swallowing study [thesis]. Barcelona: Universitat Politècnica de Catalunya; 2012.
55 Prabhu DNF, Reddy NP, Canilang EP. Neural networks for recognition of acceleration patterns during swallowing and coughing. In: 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 1997; Baltimore, MD, USA. Proceedings. New York: IEEE; 1997. p. 1105-6.
56 Cesarini V, Casiddu N, Porfirione C, Massazza G, Saggio G, Costantini G. A machine learning-based voice analysis for the detection of dysphagia biomarkers. In: 2021 IEEE International Workshop on Metrology for Industry 4.0 and IoT; 2021. Proceedings. New York: IEEE; 2021. p. 407-11.
57 Donohue C, Khalifa Y, Mao S, Perera S, Sejdic E, Coyle JL. Characterizing swallows from people with neurodegenerative diseases using high-resolution cervical auscultation signals and temporal and spatial swallow kinematic measurements. J Speech Lang Hear Res. 2021;64(9):3416-31.
58 Donohue C, Khalifa Y, Perera S, Sejdic E, Coyle JL. Characterizing effortful swallows from healthy community dwelling adults across the lifespan using high-resolution cervical auscultation signals and mbsimp scores: a preliminary study. Dysphagia. 2022;37(5):1103-11.
59 Egashira Y, Bando S, Oiwa K, Nozawa A. The attempt of swallowing discrimination by heart rate variability using machine learning. IEEJ Trans Electr Electron Eng. 2017;12(S2):S137-8.
60 Lee JH, Chee PS, Lim EH, Tan CH. Artificial intelligence-assisted throat sensor using Ionic Polymer-Metal Composite (IPMC) material. Polymers. 2021;13(18):3041.
61 Lee JT, Park E, Hwang JM, Jung TD, Park D. Machine learning analysis to automatically measure response time of pharyngeal swallowing reflex in videofluoroscopic swallowing study. Sci Rep. 2020;10(1):14735.
62 Martin-Martinez A, Miro J, Amado 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(4):1224-37.
63 Wilhelm P, Reinhardt JM, Van Daele D. A deep learning approach to video fluoroscopic swallowing exam classification. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI); 2020; Iowa City, IA, USA. Proceedings. New York: IEEE; 2020. p. 1647-50.
64 Das A, Reddy NP, Narayanan J. Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals. Comput Methods Programs Biomed. 2001;64(2):87-99.
65 Caliskan H, Mahoney AS, Coyle JL, Sejdic E. Automated bolus detection in videofluoroscopic images of swallowing using Mask-RCNN. In: 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); 2020; Montreal, QC, Canada. Proceedings. New York: IEEE; 2020. p. 2173-7.
66 Aboofazeli M, Moussavi Z. Automated classification of swallowing and breath sounds. Conf Proc IEEE Eng Med Biol Soc. 2004;2004:3816-9. PMid:17271127.
67 Kuramoto N, Ichimura K, Jayatilake D, Shimokakimoto T, Hidaka K, Suzuki K. Deep learning-based swallowing monitor for realtime detection of swallow duration. In: 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); 2020; Montreal, QC, Canada. Proceedings. New York: IEEE; 2020. p. 4365-8.
68 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(1):18754.
69 Bandini A, Smaoui S, Steele CM. Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: killing two birds with one stone? Comput Methods Programs Biomed. 2022;225:107058.
70 Fujinaka A, Mekata K, Takizawa H, Kudo H. Automated segmentation of cervical intervertebral disks from videofluorography using a convolutional neural network and its performance evaluation. J Signal Process Syst Signal Image Video Technol. 2020;92(3):299-305.
71 Hashimoto H, Kameda S, Maezawa H, Oshino S, Tani N, Khoo HM, et al. A swallowing decoder based on deep transfer learning: AlexNet classification of the intracranial electrocorticogram. Int J Neural Syst. 2021;31(11):2050056.
72 Iyer A, Thor M, Haq R, Deasy JO, Apte AP. Deep learning-based auto-segmentation of swallowing and chewing structures in CT. Phys Med Biol. 2022;67:024001.
73 Popa SL, Surdea-Blaga T, Dumitrascu DL, Chiarioni G, Savarino E, David L, et al. Automatic diagnosis of high-resolution esophageal manometry using artificial intelligence. J Gastrointestin Liver Dis. 2022;31(4):383-9.
74 Zhang F, Yu S, Chen P, Li C, Lin H, Li J, et al. Swallowing events recognition method based on complex impedance pharyngography and nyquist plots. IEEE Sens J. 2022;22(18):18076-84.
75 Zhang Z, Mao S, Coyle J, Sejdic E. Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning. Med Image Anal. 2021;74:102218.
76 Dias V, Bolzan GP. Instrumentos para captação e análise acústica dos sinais de ausculta cervical na prática clínica fonoaudiológica: uma revisão integrativa de literatura. Audiol Commun Res. 2021;26:e2498.
77 Hosaki GY, Ribeiro DF. Deep-learning: ensinando a aprender. Rev Gest Estratég. 2021;3(1):1-15.
78 Paixão GMM, Santos BC, Araujo RM, Ribeiro MH, Moraes JL, Ribeiro AL. Machine learning in medicine: review and applicability. Arq Bras Cardiol. 2022;118(1):95-102.
Submitted date:
28/09/2024
Accepted date:
16/01/2025
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