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10.1155/2021/6696357 http://scihub22266oqcxt.onion/10.1155/2021/6696357 33778081!7979280!33778081     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33778081&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Res+Int 2021 ; 2021 (ä): 6696357 Nephropedia Template TP {{tp|p=33778081|t=2021. Predicting Obesity Using Facial Pictures during COVID-19 Pandemic |pdf=|usr=}}{{33778081}} gab.com Text Predicting Obesity Using Facial Pictures during COVID-19 Pandemic JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=33778081 #FOAvip BACKGROUND: Sedentary lifestyle and work from home schedules due to the ongoing COVID-19 pandemic in 2020 have caused a significant rise in obesity across adults. With limited visits to the doctors during this period to avoid possible infections, there is currently no way to measure or track obesity. METHODS: We reviewed the literature on relationships between obesity and facial features, in white, black, hispanic-latino, and Korean populations and validated them against a cohort of Indian participants (n = 106). The body mass index (BMI) and waist-to-hip ratio (WHR) were obtained using anthropometric measurements, and body fat mass (BFM), percentage body fat (PBF), and visceral fat area (VFA) were measured using body composition analysis. Facial pictures were also collected and processed to characterize facial geometry. Regression analysis was conducted to determine correlations between body fat parameters and facial model parameters. RESULTS: Lower facial geometry was highly correlated with BMI (R (2) = 0.77) followed by PBF (R (2) = 0.72), VFA (R (2) = 0.65), WHR (R (2) = 0.60), BFM (R (2) = 0.59), and weight (R (2) = 0.54). CONCLUSIONS: The ability to predict obesity using facial images through mobile application or telemedicine can help with early diagnosis and timely medical intervention for people with obesity during the pandemic. Twit Text FOAVip Predicting Obesity Using Facial Pictures during COVID-19 Pandemic ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=33778081 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33778081 #FOAvip English Wikipedia <ref>{{Cite pmid|33778081}}</ref> Predicting Obesity Using Facial Pictures during COVID-19 Pandemic #MMPMID33778081 Chanda A; Chatterjee S Biomed Res Int 2021[]; 2021 (ä): 6696357 PMID33778081show ga BACKGROUND: Sedentary lifestyle and work from home schedules due to the ongoing COVID-19 pandemic in 2020 have caused a significant rise in obesity across adults. With limited visits to the doctors during this period to avoid possible infections, there is currently no way to measure or track obesity. METHODS: We reviewed the literature on relationships between obesity and facial features, in white, black, hispanic-latino, and Korean populations and validated them against a cohort of Indian participants (n = 106). The body mass index (BMI) and waist-to-hip ratio (WHR) were obtained using anthropometric measurements, and body fat mass (BFM), percentage body fat (PBF), and visceral fat area (VFA) were measured using body composition analysis. Facial pictures were also collected and processed to characterize facial geometry. Regression analysis was conducted to determine correlations between body fat parameters and facial model parameters. RESULTS: Lower facial geometry was highly correlated with BMI (R (2) = 0.77) followed by PBF (R (2) = 0.72), VFA (R (2) = 0.65), WHR (R (2) = 0.60), BFM (R (2) = 0.59), and weight (R (2) = 0.54). CONCLUSIONS: The ability to predict obesity using facial images through mobile application or telemedicine can help with early diagnosis and timely medical intervention for people with obesity during the pandemic. |Adult[MESH] |Anthropometry/*methods[MESH] |Automated Facial Recognition/*methods[MESH] |Body Composition[MESH] |Body Mass Index[MESH] |Body Weight[MESH] |COVID-19/*epidemiology[MESH] |Facial Recognition/physiology[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Obesity/*diagnosis/epidemiology/metabolism[MESH] |Pandemics[MESH] |Predictive Value of Tests[MESH] |Prognosis[MESH] |Risk Factors[MESH] |SARS-CoV-2/isolation & purification[MESH] |Waist Circumference[MESH]Predicting Obesity Using Facial Pictures during COVID-19 Pandemic (epmc).pdf DeepDyve Pubget Overpricing