Real-time and digital remote nutritional assessment framework with the use of smartphone-enabled facial morphometrics and machine learning— a proof of concept

De Araujo BE, Kowalski V, Leites GM, da Silva Fink J, Silva FM. AND-ASPEN and ESPEN consensus, and GLIM criteria for malnutrition identification in AECOPD patients: a longitudinal study comparing concurrent and predictive validity. Eur J Clin Nutr. 2022;76:685–92.

Article  PubMed  Google Scholar 

Juby AG, Mager DR. A review of nutrition screening tools used to assess the malnutrition-sarcopenia syndrome (MSS) in the older adult. Clin Nutr ESPEN. 2019;32:8–15.

Article  PubMed  Google Scholar 

de Juras AR, Hu SC. A review on dietary patterns and double burden of malnutrition: knowledge gaps for future research. Asia Pac J Public Health. 2023;35:7–13.

Article  PubMed  Google Scholar 

Upadhyaya AN, Saqib A, Devi JV, Rallapalli S, Sudha S, Boopathi S. Implementation of the Internet of Things (IoT) in Remote Healthcare. Analyzing Current Digital Healthcare Trends Using Social Networks: IGI Global; 2024. p. 104–24.

Kelly J, Collins P, McCamley J, Ball L, Roberts S, Campbell K. Digital disruption of dietetics: are we ready?. J Hum Nutr Dietetics. 2021;34:134–46.

Article  CAS  Google Scholar 

Krznarić Ž, Bender DV, Laviano A, Cuerda C, Landi F, Monteiro R. et al. A simple remote nutritional screening tool and practical guidance for nutritional care in primary practice during the COVID-19 pandemic. Clinical Nutrition. 2020;39:1983–7.

Article  PubMed  Google Scholar 

Limketkai BN, Mauldin K, Manitius N, Jalilian L, Salonen BR. The age of artificial intelligence: use of digital technology in clinical nutrition. Curr Surg Rep. 2021;9:20.

Article  PubMed  PubMed Central  Google Scholar 

Agbolade O, Nazri A, Yaakob R, Ghani AA, Cheah YK. Morphometric approach to 3D soft-tissue craniofacial analysis and classification of ethnicity, sex, and age. PLoS ONE. 2020;15:e0228402.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Finka LR, Luna SP, Brondani JT, Tzimiropoulos Y, McDonagh J, Farnworth MJ, et al. Geometric morphometrics for the study of facial expressions in non-human animals, using the domestic cat as an exemplar. Sci Rep. 2019;9:1–12.

Article  CAS  Google Scholar 

Coetzee V, Perrett DI, Stephen ID. Facial adiposity: A cue to health?. Perception. 2009;38:1700–11.

Article  PubMed  Google Scholar 

Foo YZ, Simmons LW, Rhodes G. Predictors of facial attractiveness and health in humans. Sci Rep. 2017;7:39731.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Henderson AJ, Holzleitner IJ, Talamas SN, Perrett DI. Perception of health from facial cues. Philos Trans R Soc B: Biol Sci. 2016;371:20150380.

Article  Google Scholar 

Kaur M, Garg RK, Singla S. Analysis of facial soft tissue changes with aging and their effects on facial morphology: A forensic perspective. Egypt J Forensic Sci. 2015;5:46–56.

Article  Google Scholar 

Stephen ID, Hiew V, Coetzee V, Tiddeman BP, Perrett DI. Facial shape analysis identifies valid cues to aspects of physiological health in Caucasian, Asian, and African populations. Front Psychol. 2017;8:1883.

Article  PubMed  PubMed Central  Google Scholar 

https://developer.apple.com/documentation/arkit/content_anchors/tracking_and_visualizing_faces Tracking and Visualizing Faces: Apple; 2022.

Ang C-S Filial piety, Caregiving Self-Efficacy, and Caregiver Burden in Sandwich Generation Caregivers: What Are the Relationships? Home Health Care Management & Practice. 2024:10848223241279298.

Eaton-Evans J. Nutritional Assessment: Anthropometry. 2013. p. 227–32.

Shi W, Neubeck L, Gallagher R. Measurement matters: A systematic review of waist measurement sites for determining central adiposity. Collegian. 2017;24:513–23.

Article  Google Scholar 

Hida T, Ando K, Kobayashi K, Ito K, Tsushima M, Kobayakawa T, et al. Ultrasound measurement of thigh muscle thickness for assessment of sarcopenia. Nagoya J Med Sci. 2018;80:519.

PubMed  PubMed Central  Google Scholar 

Paulsen RR, Juhl KA, Haspang TM, Hansen T, Ganz M, Einarsson G, editors. Multi-view consensus CNN for 3D facial landmark placement. Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part I; 2019: Springer.

Fagertun J, Harder S, Rosengren A, Moeller C, Werge T, Paulsen RR, et al. 3D facial landmarks: Inter-operator variability of manual annotation. BMC Med Imaging. 2014;14:1–9.

Article  Google Scholar 

Yin L, Wei X, Sun Y, Wang J, Rosato MJ, editors. A 3D facial expression database for facial behavior research. 7th International Conference on Automatic Face and Gesture Recognition (FGR06); 2006: IEEE.

Tay W, Quek R, Kaur B, Lim J, Henry CJ. Use of Facial Morphology to determine nutritional status in older adults: opportunities and challenges. JMIR Public Health Surveill. 2022;8:e33478.

Article  PubMed  PubMed Central  Google Scholar 

Ho TK, editor Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition; 1995: IEEE.

Chen T, Guestrin C, editors. Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM sigkdd International Conference on Knowledge Discovery and Data Mining; 2016.

Berrar D. Cross-Validation. In: Ranganathan S, Gribskov M, Nakai K, Schönbach C, editors. Encyclopedia of Bioinformatics and Computational Biology. Vol. 1. Elsevier; 2019. p. 542–545. https://doi.org/10.1016/B978-0-12-809633-8.20349-X.

Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput Sci. 2021;7:e623.

Article  PubMed  PubMed Central  Google Scholar 

Breiman L. Manual on setting up, using, and understanding random forests v3. 1. Stat Dep Univ Calif Berkeley, CA, USA. 2002;1:3–42.

Google Scholar 

Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Proc Syst. 2017;30:1–10.

Google Scholar 

S.M. L. SHAP (SHapley Additive exPlanations) 2022 [Available from: https://github.com/slundberg/shap.

Moeng-Mahlangu L, Monyeki MA, Reilly JJ, Kruger HS. Comparison of Several Prediction Equations Using Skinfold Thickness for Estimating Percentage Body Fat vs. Body Fat Percentage Determined by BIA in 6–8-Year-Old South African Children: The BC–IT Study. Int J Environ Res Public Health. 2022;19:14531.

Article  PubMed  PubMed Central  Google Scholar 

Ranasinghe C, Gamage P, Katulanda P, Andraweera N, Thilakarathne S, Tharanga P. Relationship between body mass index (BMI) and body fat percentage, estimated by bioelectrical impedance, in a group of Sri Lankan adults: a cross sectional study. BMC Public Health. 2013;13:1–8.

Article  Google Scholar 

Haider S, Luger E, Kapan A, Titze S, Lackinger C, Schindler KE, et al. Associations between daily physical activity, handgrip strength, muscle mass, physical performance and quality of life in prefrail and frail community-dwelling older adults. Qual Life Res. 2016;25:3129–38.

Article  PubMed  PubMed Central  Google Scholar 

Hiol AN, von Hurst PR, Conlon CA, Mugridge O, Beck KL. Body composition associations with muscle strength in older adults living in Auckland, New Zealand. PLoS ONE. 2021;16:e0250439.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Samouda H, Dutour A, Chaumoitre K, Panuel M, Dutour O, Dadoun F. VAT= TAAT-SAAT: Innovative anthropometric model to predict visceral adipose tissue without resort to CT-Scan or DXA. Obesity. 2013;21:E41–E50.

Article  PubMed  Google Scholar 

Dantcheva A, Bremond F, Bilinski P, editors. Show me your face and I will tell you your height, weight and body mass index. 2018 24th International Conference on Pattern Recognition (ICPR); 2018: IEEE.

Haritosh A, Gupta A, Chahal ES, Misra A, Chandra S, editors. A novel method to estimate Height, Weight and Body Mass Index from face images. 2019 Twelfth International Conference on Contemporary Computing (IC3); 2019: IEEE.

Jiang M, Guo G, Mu G. Visual BMI estimation from face images using a label distribution based method. Comput Vis Image Underst. 2020;197:102985.

Article  Google Scholar 

Kocabey E, Ofli F, Marin J, Torralba A, Weber I, editors. Using computer vision to study the effects of BMI on online popularity and weight-based homophily. International Conference on Social Informatics; 2018: Springer.

Chanda A, Chatterjee S. Predicting Obesity Using Facial Pictures during COVID-19 Pandemic. BioMed Res Int. 2021;2021:1–7.

Article  Google Scholar 

Barr ML, Guo G, Colby SE, Olfert MD. Detecting body mass index from a facial photograph in lifestyle intervention. Technologies. 2018;6:83.

Article  Google Scholar 

Bidani S, Priya RP, Vijayarajan V, Prasath V. Automatic body mass index detection using correlation of face visual cues. Technol Health Care. 2020;28:107–12.

Article  PubMed  Google Scholar 

Hasegawa Y, Yoshida M, Sato A, Fujimoto Y, Minematsu T, Sugama J, et al. Temporal muscle thickness as a new indicator of nutritional status in older individuals. Geriatr Gerontol Int. 2019;19:135–40.

Article  PubMed  Google Scholar 

Hwang Y, Lee YH, Cho DH, Kim M, Lee D-S, Cho HJ. Applicability of the masseter muscle as a nutritional biomarker. Medicine. 2020;99:e19069.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Re DE, Rule NO. About face: New directions for the Physician’s General Survey. Curr Direct Psychol Sci. 2016;25:65–9.

Article  Google Scholar 

Phalane KG, Tribe C, Steel HC, Cholo MC, Coetzee V. Facial appearance reveals immunity in African men. Sci Rep. 2017;7:7443.

Article  PubMed  PubMed Central  Google Scholar 

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