Treffer: Self-helped detection of obstructive sleep apnea based on automated facial recognition and machine learning.
Title:
Self-helped detection of obstructive sleep apnea based on automated facial recognition and machine learning.
Authors:
Chen Q; Sleep Medical Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China., Liang Z; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China., Wang Q; Department of Automation, Tsinghua University, Beijing, China.; Pharmacovigilance Research Center for Information Technology and Data Science, Cross-Strait Tsinghua Research Institute, Xiamen, China., Ma C; Sleep Medical Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.; Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, China., Lei Y; School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, China., Sanderson JE; Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, China., Hu X; Automation School, Beijing University of Posts and Telecommunications, Beijing, China., Lin W; Automation School, Beijing University of Posts and Telecommunications, Beijing, China., Liu H; Sleep Medical Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China., Xie F; Sleep Medical Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China., Jiang H; Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, China. jhf@pku.edu.cn., Fang F; Sleep Medical Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China. fangfang_ff@hotmail.com.; Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, China. fangfang_ff@hotmail.com.
Source:
Sleep & breathing = Schlaf & Atmung [Sleep Breath] 2023 Dec; Vol. 27 (6), pp. 2379-2388. Date of Electronic Publication: 2023 Jun 06.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Country of Publication: Germany NLM ID: 9804161 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-1709 (Electronic) Linking ISSN: 15209512 NLM ISO Abbreviation: Sleep Breath Subsets: MEDLINE
Imprint Name(s):
Publication: 2005- : Heidelberg ; New York : Springer
Original Publication: Titisee-Neustadt, Germany : Druckbild GmbH,
Original Publication: Titisee-Neustadt, Germany : Druckbild GmbH,
MeSH Terms:
References:
Mannarino MR, Di Filippo F, Pirro M (2012) Obstructive sleep apnea syndrome. Eur J Intern Med 23:586–593. (PMID: 10.1016/j.ejim.2012.05.01322939801)
Sutherland K, Lee RWW, Cistulli PA (2012) Obesity and craniofacial structure as risk factors for obstructive sleep apnoea: impact of ethnicity. Respirology 17:213–222. (PMID: 10.1111/j.1440-1843.2011.02082.x21992683)
Xu L, Keenan BT, Wiemken AS, Chi L, Staley B, Wang Z, Wang J, Benedikstdottir B, Juliusson S, Pack AI, Gislason T, Schwab RJ (2020) Differences in three-dimensional upper airway anatomy between Asian and European patients with obstructive sleep apnea. Sleep 43(5):zsz273.
Lee RWW (2010) Differences in craniofacial structures and obesity in Caucasian and Chinese patients with obstructive sleep apnea. Sleep 33(8):1075–1080. (PMID: 10.1093/sleep/33.8.1075208151892910536)
Sutherland K, Lee RWW, Chan TO, Ng S, Hui DS, Cistulli PA (2018) Craniofacial phenotyping in Chinese and Caucasian patients with sleep apnea: influence of ethnicity and sex. J Clin Sleep Med 14:1143–1151. (PMID: 10.5664/jcsm.7212299914236040806)
Sutherland K, Chapman JL, Cayanan EA, Lowth AB, Wong KKH, Yee BJ, Grunstein RR, Marshall NS, Cistulli PA (2019) Parsing the craniofacial phenotype: effect of weight change in an obstructive sleep apnoea population. Sleep Breath 23:1291–1298. (PMID: 10.1007/s11325-019-01826-230927176)
O'driscoll DM, Landry SA, Pham J, Young A, Sands SA, Hamilton GS, Edwards BA (2019) The physiological phenotype of obstructive sleep apnea differs between Caucasian and Chinese patients. Sleep 42(11):zsz186.
Neelapu BC, Kharbanda OP, Sardana HK, Balachandran R, Sardana V, Kapoor P, Gupta A, Vasamsetti S (2017) Craniofacial and upper airway morphology in adult obstructive sleep apnea patients: a systematic review and meta-analysis of cephalometric studies. Sleep Med Rev 31:79–90. (PMID: 10.1016/j.smrv.2016.01.00727039222)
Sutherland K, Phillips CL, Yee BJ, Grunstein RR, Cistulli AA (2016) Maxillomandibular volume influences the relationship between weight loss and improvement in obstructive sleep apnea. Sleep 39:43–49. (PMID: 10.5665/sleep.5314263504704678342)
Yung-Chuan Liu S (2016) Static craniofacial measurements and dynamic airway collapse patterns associated with severe obstructive sleep apnoea: a sleep MRI study. Clin Otolaryngol 41(6):700–706. (PMID: 10.1111/coa.12598)
Lee RWW (2009) Craniofacial phenotyping in obstructive sleep apnea – a novel quantitative photographic approach. Sleep 32(1):37–45. (PMID: 191897772625322)
Sutherland K, Lee RWW, Petocz P, Chan TO, Ng S, Hui DS, Cistulli PA (2016) Craniofacial phenotyping for prediction of obstructive sleep apnoea in a Chinese population. Respirology 21:1118–1125. (PMID: 10.1111/resp.1279227083503)
Remya KJ, Mathangi K, Mathangi DC, Sriteja Y, Srihari R, Govindaraju S, Hillman DR, Eastwood PR (2017) Predictive value of craniofacial and anthropometric measures in obstructive sleep apnea (OSA). Cranio : J Craniomandibular Pract 35:162–167. (PMID: 10.1080/08869634.2016.1206701)
Huang W-C, Lee P-L, Liu Y-T, Chiang AA, Lai F (2020) Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample. Sleep 43(7):zsz295.
Myers KA, Mrkobrada M, Simel DL (2013) Does this patient have obstructive sleep apnea?: The Rational Clinical Examination systematic review. JAMA 310:731–741. (PMID: 10.1001/jama.2013.27618523989984)
Kapur VK, Auckley DH, Chowdhuri S, Kuhlmann DC, Mehra R, Ramar K, Harrod CG (2017) Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: an American Academy of Sleep Medicine Clinical Practice Guideline. J Clin Sleep Med 13:479–504. (PMID: 10.5664/jcsm.6506281621505337595)
Kazemi V, Sullivan J (2014) One millisecond face alignment with an ensemble of regression trees. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA. pp. 1867–1874.
Coutinho Costa J, Rebelo-Marques A, Machado JN, Gama JMR, Santos C, Teixeira F, Moita J (2019) Validation of NoSAS (neck, obesity, snoring, age, sex) score as a screening tool for obstructive sleep apnea: analysis in a sleep clinic. Pulmonology 25:263–270. (PMID: 10.1016/j.pulmoe.2019.04.00431196834)
Lin S-W, Sutherland K, Liao F-U, Cistulli PA, Chuang L-P, Chou Y-T, Chang C-H, Lee CS, Li LF, Chen NH (2018) Three-dimensional photography for the evaluation of facial profiles in obstructive sleep apnoea. Respirology 23:618–625. (PMID: 10.1111/resp.1326129462843)
Banabilh SM, Suzina AH, Dinsuhaimi S, Samsudin AR, Singh GD (2009) Craniofacial obesity in patients with obstructive sleep apnea. Sleep Breath 13:19–24. (PMID: 10.1007/s11325-008-0211-918763003)
Sutherland K, Schwab RJ, Maislin G, Lee RWW, Benedikstdsottir B, Pack AI, Gislason T, Juliusson S, Cistulli PA (2014) Facial phenotyping by quantitative photography reflects craniofacial morphology measured on magnetic resonance imaging in Icelandic sleep apnea patients. Sleep 37:959–968. (PMID: 10.5665/sleep.3670247902753985099)
Tondo P, Dell’Olio F, Lacedonia D, Sabato R, Leccisotti R, Foschino Barbaro MP, Scioscia G (2022) A consumer wearable device for tracking sleep respiratory events. Sleep Breath 2022:Online ahead of print.
Holfinger SJ, Lyons MM, Keenan BT, Mazzotti DR, Mindel J, Maislin G, Cistulli PA, Sutherland K, Mcardle N, Singh B, Chen NH, Gislason T, Penzel T, Han F, Li QY, Schwab R, Pack AI, Magalang UJ (2022) Diagnostic performance of machine learning-derived OSA prediction tools in large clinical and community-based samples. Chest 161:807–817. (PMID: 10.1016/j.chest.2021.10.02334717928)
He S, Li Y, Xu W, Han D (2022) Using clinical data to predict obstructive sleep apnea. J Thorac Dis 14:227–237.
Gourishetti SC, Taylor R, Isaiah A (2022) Stratifying the risk of cardiovascular disease in obstructive sleep apnea using machine learning. Laryngoscope 132(1):234–241.
Espinoza-Cuadros F, Fernandez-Pozo R, Toledano DT, Alcazar-Ramirez JD, Lopez-Gonzalo E, Hernandez-Gomez LA (2015) Speech signal and facial image processing for obstructive sleep apnea assessment. Comput Math Methods Med 2015:489761.
Tyan M, Espinoza-Cuadros F, Fernández Pozo R, Toledano D, Lopez Gonzalo E, Alcazar Ramirez JD, Hernandez Gomez LA (2017) Obstructive sleep apnea in women: study of speech and craniofacial characteristics. JMIR mHealth and uHealth 5:e169. (PMID: 10.2196/mhealth.8238291090685696580)
Ozdemir ST (2019) Three-dimensional analysis of craniofacial shape in obstructive sleep apnea syndrome using geometric morphometrics. Int J Morphol 37(1):338–343.
Wang X, Chen H, Jia L, Xu X, Guo J (2021) The relationship between three-dimensional craniofacial and upper airway anatomical variables and severity of obstructive sleep apnoea in adults. Eur J Orthod 44(1):78–85.
Monna F, Ben Messaoud R, Navarro N, Baillieul S, Sanchez L, Loiodice C, Tamisier R, Joyeux-Faure M, Pepin JF (2022) Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans. Sleep Med 95:76–83. (PMID: 10.1016/j.sleep.2022.04.01935567881)
Sutherland K, Lee RWW, Cistulli PA (2012) Obesity and craniofacial structure as risk factors for obstructive sleep apnoea: impact of ethnicity. Respirology 17:213–222. (PMID: 10.1111/j.1440-1843.2011.02082.x21992683)
Xu L, Keenan BT, Wiemken AS, Chi L, Staley B, Wang Z, Wang J, Benedikstdottir B, Juliusson S, Pack AI, Gislason T, Schwab RJ (2020) Differences in three-dimensional upper airway anatomy between Asian and European patients with obstructive sleep apnea. Sleep 43(5):zsz273.
Lee RWW (2010) Differences in craniofacial structures and obesity in Caucasian and Chinese patients with obstructive sleep apnea. Sleep 33(8):1075–1080. (PMID: 10.1093/sleep/33.8.1075208151892910536)
Sutherland K, Lee RWW, Chan TO, Ng S, Hui DS, Cistulli PA (2018) Craniofacial phenotyping in Chinese and Caucasian patients with sleep apnea: influence of ethnicity and sex. J Clin Sleep Med 14:1143–1151. (PMID: 10.5664/jcsm.7212299914236040806)
Sutherland K, Chapman JL, Cayanan EA, Lowth AB, Wong KKH, Yee BJ, Grunstein RR, Marshall NS, Cistulli PA (2019) Parsing the craniofacial phenotype: effect of weight change in an obstructive sleep apnoea population. Sleep Breath 23:1291–1298. (PMID: 10.1007/s11325-019-01826-230927176)
O'driscoll DM, Landry SA, Pham J, Young A, Sands SA, Hamilton GS, Edwards BA (2019) The physiological phenotype of obstructive sleep apnea differs between Caucasian and Chinese patients. Sleep 42(11):zsz186.
Neelapu BC, Kharbanda OP, Sardana HK, Balachandran R, Sardana V, Kapoor P, Gupta A, Vasamsetti S (2017) Craniofacial and upper airway morphology in adult obstructive sleep apnea patients: a systematic review and meta-analysis of cephalometric studies. Sleep Med Rev 31:79–90. (PMID: 10.1016/j.smrv.2016.01.00727039222)
Sutherland K, Phillips CL, Yee BJ, Grunstein RR, Cistulli AA (2016) Maxillomandibular volume influences the relationship between weight loss and improvement in obstructive sleep apnea. Sleep 39:43–49. (PMID: 10.5665/sleep.5314263504704678342)
Yung-Chuan Liu S (2016) Static craniofacial measurements and dynamic airway collapse patterns associated with severe obstructive sleep apnoea: a sleep MRI study. Clin Otolaryngol 41(6):700–706. (PMID: 10.1111/coa.12598)
Lee RWW (2009) Craniofacial phenotyping in obstructive sleep apnea – a novel quantitative photographic approach. Sleep 32(1):37–45. (PMID: 191897772625322)
Sutherland K, Lee RWW, Petocz P, Chan TO, Ng S, Hui DS, Cistulli PA (2016) Craniofacial phenotyping for prediction of obstructive sleep apnoea in a Chinese population. Respirology 21:1118–1125. (PMID: 10.1111/resp.1279227083503)
Remya KJ, Mathangi K, Mathangi DC, Sriteja Y, Srihari R, Govindaraju S, Hillman DR, Eastwood PR (2017) Predictive value of craniofacial and anthropometric measures in obstructive sleep apnea (OSA). Cranio : J Craniomandibular Pract 35:162–167. (PMID: 10.1080/08869634.2016.1206701)
Huang W-C, Lee P-L, Liu Y-T, Chiang AA, Lai F (2020) Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample. Sleep 43(7):zsz295.
Myers KA, Mrkobrada M, Simel DL (2013) Does this patient have obstructive sleep apnea?: The Rational Clinical Examination systematic review. JAMA 310:731–741. (PMID: 10.1001/jama.2013.27618523989984)
Kapur VK, Auckley DH, Chowdhuri S, Kuhlmann DC, Mehra R, Ramar K, Harrod CG (2017) Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: an American Academy of Sleep Medicine Clinical Practice Guideline. J Clin Sleep Med 13:479–504. (PMID: 10.5664/jcsm.6506281621505337595)
Kazemi V, Sullivan J (2014) One millisecond face alignment with an ensemble of regression trees. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA. pp. 1867–1874.
Coutinho Costa J, Rebelo-Marques A, Machado JN, Gama JMR, Santos C, Teixeira F, Moita J (2019) Validation of NoSAS (neck, obesity, snoring, age, sex) score as a screening tool for obstructive sleep apnea: analysis in a sleep clinic. Pulmonology 25:263–270. (PMID: 10.1016/j.pulmoe.2019.04.00431196834)
Lin S-W, Sutherland K, Liao F-U, Cistulli PA, Chuang L-P, Chou Y-T, Chang C-H, Lee CS, Li LF, Chen NH (2018) Three-dimensional photography for the evaluation of facial profiles in obstructive sleep apnoea. Respirology 23:618–625. (PMID: 10.1111/resp.1326129462843)
Banabilh SM, Suzina AH, Dinsuhaimi S, Samsudin AR, Singh GD (2009) Craniofacial obesity in patients with obstructive sleep apnea. Sleep Breath 13:19–24. (PMID: 10.1007/s11325-008-0211-918763003)
Sutherland K, Schwab RJ, Maislin G, Lee RWW, Benedikstdsottir B, Pack AI, Gislason T, Juliusson S, Cistulli PA (2014) Facial phenotyping by quantitative photography reflects craniofacial morphology measured on magnetic resonance imaging in Icelandic sleep apnea patients. Sleep 37:959–968. (PMID: 10.5665/sleep.3670247902753985099)
Tondo P, Dell’Olio F, Lacedonia D, Sabato R, Leccisotti R, Foschino Barbaro MP, Scioscia G (2022) A consumer wearable device for tracking sleep respiratory events. Sleep Breath 2022:Online ahead of print.
Holfinger SJ, Lyons MM, Keenan BT, Mazzotti DR, Mindel J, Maislin G, Cistulli PA, Sutherland K, Mcardle N, Singh B, Chen NH, Gislason T, Penzel T, Han F, Li QY, Schwab R, Pack AI, Magalang UJ (2022) Diagnostic performance of machine learning-derived OSA prediction tools in large clinical and community-based samples. Chest 161:807–817. (PMID: 10.1016/j.chest.2021.10.02334717928)
He S, Li Y, Xu W, Han D (2022) Using clinical data to predict obstructive sleep apnea. J Thorac Dis 14:227–237.
Gourishetti SC, Taylor R, Isaiah A (2022) Stratifying the risk of cardiovascular disease in obstructive sleep apnea using machine learning. Laryngoscope 132(1):234–241.
Espinoza-Cuadros F, Fernandez-Pozo R, Toledano DT, Alcazar-Ramirez JD, Lopez-Gonzalo E, Hernandez-Gomez LA (2015) Speech signal and facial image processing for obstructive sleep apnea assessment. Comput Math Methods Med 2015:489761.
Tyan M, Espinoza-Cuadros F, Fernández Pozo R, Toledano D, Lopez Gonzalo E, Alcazar Ramirez JD, Hernandez Gomez LA (2017) Obstructive sleep apnea in women: study of speech and craniofacial characteristics. JMIR mHealth and uHealth 5:e169. (PMID: 10.2196/mhealth.8238291090685696580)
Ozdemir ST (2019) Three-dimensional analysis of craniofacial shape in obstructive sleep apnea syndrome using geometric morphometrics. Int J Morphol 37(1):338–343.
Wang X, Chen H, Jia L, Xu X, Guo J (2021) The relationship between three-dimensional craniofacial and upper airway anatomical variables and severity of obstructive sleep apnoea in adults. Eur J Orthod 44(1):78–85.
Monna F, Ben Messaoud R, Navarro N, Baillieul S, Sanchez L, Loiodice C, Tamisier R, Joyeux-Faure M, Pepin JF (2022) Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans. Sleep Med 95:76–83. (PMID: 10.1016/j.sleep.2022.04.01935567881)
Grant Information:
2020YFC2003600 the National Key Research and Development Program of China
Contributed Indexing:
Keywords: Craniofacial feature; Machine learning; Obstructive sleep apnea; Photogrammetry; Predictive model
Entry Date(s):
Date Created: 20230606 Date Completed: 20231127 Latest Revision: 20231127
Update Code:
20250114
DOI:
10.1007/s11325-023-02846-9
PMID:
37278870
Database:
MEDLINE
Weitere Informationen
Purpose: The diagnosis of obstructive sleep apnea (OSA) relies on time-consuming and complicated procedures which are not always readily available and may delay diagnosis. With the widespread use of artificial intelligence, we presumed that the combination of simple clinical information and imaging recognition based on facial photos may be a useful tool to screen for OSA.
Methods: We recruited consecutive subjects suspected of OSA who had received sleep examination and photographing. Sixty-eight points from 2-dimensional facial photos were labelled by automated identification. An optimized model with facial features and basic clinical information was established and tenfold cross-validation was performed. Area under the receiver operating characteristic curve (AUC) indicated the model's performance using sleep monitoring as the reference standard.
Results: A total of 653 subjects (77.2% males, 55.3% OSA) were analyzed. CATBOOST was the most suitable algorithm for OSA classification with a sensitivity, specificity, accuracy, and AUC of 0.75, 0.66, 0.71, and 0.76 respectively (P < 0.05), which was better than STOP-Bang questionnaire, NoSAS scores, and Epworth scale. Witnessed apnea by sleep partner was the most powerful variable, followed by body mass index, neck circumference, facial parameters, and hypertension. The model's performance became more robust with a sensitivity of 0.94, for patients with frequent supine sleep apnea.
Conclusion: The findings suggest that craniofacial features extracted from 2-dimensional frontal photos, especially in the mandibular segment, have the potential to become predictors of OSA in the Chinese population. Machine learning-derived automatic recognition may facilitate the self-help screening for OSA in a quick, radiation-free, and repeatable manner.
(© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)