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DiP – FitYourOutfit®

Cos’è Il Forum Sistema Salute – “AI Best in Healthcare”

Il Forum Sistema Salute – “AI Best in Healthcare” 2023 ha come scopo quello di mettere in evidenza e valorizzare progetti e soluzioni che utilizzano l’intelligenza artificiale nel mondo sanitario.
Nell’ambito di questo importante evento tenutosi il 19-20 ottobre scorso presso la storica Leopolda di Firenze, una tecnologia innovativa ha ricevuto un riconoscimento speciale per il suo impatto rivoluzionario sulla salute e il benessere.

FitYourOutfit by Pixelcando

Nell’ambito di questo importante evento tenutosi il 19-20 ottobre scorso presso la storica Leopolda di Firenze, l’azienda PIXELCANDO SL, proprietaria del metodo e marchio FitYourOutfit (FYO), ha ricevuto un riconoscimento speciale classificandosi al 3° posto evidenziando l’importanza della sua metodica per la prevenzione del rischio cardio-metabolico.

Scopriamo come FYO sta rivoluzionando il modo nel quale è possibile affrontare questa importante sfida sanitaria.

Lexa Nescolarde 1 , Carmine Orlandi 2,3, Gian Luca Farina 3, Niccolo’ Gori 4 and Henry Lukaski 5

1 Department of Electronic Engineering, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
2 Medical Faculty, Tor Vergata University, 00133 Rome, Italy;
3 Medical Center Eubion, 00135 Rome, Italy;
4 Federazione Italiana Rugby—FIR, Stadio Olimpico, Foro Italico, 00135 Rome, Italy;
5 Department of Kinesiology and Public Health Education, University of North Dakota,
Grand Forks, ND 58201, USA;

Abstract:
The need for a practical method for routine determination of body fat has progressed from body mass index (BMI) to bioelectrical impedance analysis (BIA) and smartphone two-dimensional imaging. We determined agreement in fat mass (FM) estimated with 50 kHz BIA and smartphone single lateral standing digital image (SLSDI) compared to dual X-ray absorptiometry (DXA) in 188 healthy adults (69 females and 119 males). BIA underestimated (p < 0.0001) FM, whereas SLSDI FM estimates were not different from DXA values. Based on limited observations that BIA overestimated fat-free mass (FFM) in obese adults, we tested the hypothesis that expansion of the extracellular water (ECW), expressed as ECW to intracellular water (ECW/ICW), results in underestimation of BIA-dependent FM. Using a general criterion of BMI > 25 kg/m2, 54 male rugby players, compared to 40 male non-rugby players, had greater (p < 0.001) BMI and FFM but less (p < 0.001) FM and ECW/ICW. BIA underestimated (p < 0.001) FM in the non-rugby men, but SLSDI and DXA FM estimates were not different in both groups. This finding is consistent with the expansion of ECW in individuals with excess body fat due to increased adipose tissue mass and its water content. Unlike SLSDI, 50 kHz BIA predictions of FM are affected by an increased ECW/ICW associated with greater adipose tissue. These findings demonstrate the validity, practicality, and convenience of smartphone SLSDI to estimate FM, seemingly not influenced by variable hydration states, for healthcare providers in clinical and field settings.

La necessità di un metodo pratico per la determinazione di routine del grasso corporeo è passata dall’indice di massa corporea (BMI) all’analisi dell’impedenza bioelettrica (BIA) e all’imaging bidimensionale tramite smartphone. Abbiamo determinato l’accordo nella massa grassa (FM) stimata con BIA a 50 kHz e immagine digitale in posizione laterale singola da smartphone (SLSDI) rispetto all’assorbimetria a raggi X doppia (DXA) in 188 adulti sani (69 femmine e 119 maschi). La BIA ha sottostimato (p <0,0001) FM, mentre le stime SLSDI FM non erano diverse dai valori DXA. Sulla base di osservazioni limitate secondo cui la BIA sovrastima la massa magra (FFM) negli adulti obesi, abbiamo testato l’ipotesi che l’espansione dell’acqua extracellulare (ECW), espressa come ECW in acqua intracellulare (ECW/ICW), si traduce in una sottostima della BIA-FM dipendente. Utilizzando un criterio generale di BMI > 25 kg/m2, 54 giocatori di rugby maschi, rispetto a 40 giocatori non di rugby, avevano BMI e FFM maggiori (p < 0,001) ma meno FM ed ECW/ICW (p < 0,001). La BIA ha sottostimato (p < 0,001) la FM negli uomini non di rugby, ma le stime SLSDI e DXA FM non erano diverse in entrambi i gruppi. Questo risultato è coerente con l’espansione dell’ECW negli individui con grasso corporeo in eccesso a causa dell’aumento della massa del tessuto adiposo e del suo contenuto di acqua. A differenza di SLSDI, le previsioni di FM della BIA a 50 kHz sono influenzate da un aumento di ECW/ICW associato a un maggiore tessuto adiposo.
Questi risultati dimostrano la validità, la praticità e la comodità dell’SLSDI per smartphone per stimare la FM, apparentemente non influenzata da stati di idratazione variabili, per gli operatori sanitari in contesti clinici e sul campo.

CONCLUSIONS:
Smartphone SLSDI provides highly comparable DXA estimates of FM of adults with a wide range of body fat.
The agreement of SLSDI in the estimation of FM compared to DXA, coupled with the convenience, practicality, and cost-efficiency, facilitate an innovative method to assess body composition to enable routine assessment of body fat for healthcare personnel in many environments and for biomedical researchers outside the laboratory.
Importantly, the SLSDI method does not require specialized equipment, only generally available smartphones. It surmounts the limitations of BMI as an index of body fat and provides a novel method to estimate abdominal FM [22]. Moreover, SLSDI image-based FM estimates provide overall better performances than impedance-based methods
because they are seemingly not influenced by variable hydration states.

CONCLUSIONI:
SLSDI per smartphone fornisce stime DXA altamente comparabili di FM di adulti con un’ampia gamma di grasso corporeo. L’accordo di SLSDI nella stima della FM rispetto alla DXA, unito alla praticità, praticità ed efficienza in termini di costi, facilitano un metodo innovativo per valutare la composizione corporea per consentire la valutazione di routine del grasso corporeo per il personale sanitario in molti ambienti e per i ricercatori biomedici fuori dal laboratorio. È importante sottolineare che il metodo SLSDI non richiede attrezzature specializzate, ma solo smartphone generalmente disponibili. Supera i limiti del BMI come indice di grasso corporeo [1,2,3] e fornisce un nuovo metodo per stimare la FM addominale [4]. Inoltre, le stime FM basate su immagini SLSDI forniscono prestazioni complessivamente migliori rispetto ai metodi basati sull’impedenza perché apparentemente non sono influenzate da stati di idratazione variabili.

Gian Luca Farina * , Carmine Orlandi , Niccolò Gori , Lexa Nescolarde , Henry Lukaski

Abstract: Limitations of body mass index (BMI) as a measure of body fat and the need for practical methods to estimate body fat reinforce interest in smartphone two-dimensional digital imaging and bioelectrical impedance analysis (BIA). Compared to dual x-ray absorptiometry (DXA), we determined differences in body fat mass (FM) estimated with smartphone single lateral standing digital image (SLSDI) and bioimpedance analysis (BIA) in 188 healthy adults (69 females and 119 males). SLSDI FM estimates were similar to DXA values but BIA underestimated (p<0.0001) FM. We tested the hypothesis that fluid imbalance, expansion of the extracellular water (ECW), designated as ECW to intracellular water ratio (ECW/ICW), affects the BIAdependent differences. With BMI>25 kg/m2, 54 male rugby players, compared to 40 male non-rugby players, had greater (p<0.001) BMI and fat-free mass but less (p<0.001) FM and ECW/ICW. SLSDI and DXA FM estimates were not different in both groups; BIA underestimated (p<0.001) FM in the non-rugby men. This finding is consistent with expansion of ECW in individuals with excess body fat due to increased adipose tissue mass and its water content. Unlike SLSDI, BIA predictions of FM are affected by altered fluid distribution associated with increased adipose tissue. These findings establish the validity, practicality, and convenience of smartphone SLSDI to estimate FM for healthcare providers in clinical and field settings.

by Gian Luca Farina, Carmine Orlandi, Henry Lukaski and Lexa Nescolarde

Sensor: https://www.ncbi.nlm.nih.gov/pubmed/16687022

Abstract

Background: Obesity is chronic health problem. Screening for the obesity phenotype is limited by the availability of practical methods. Methods: We determined the reproducibility and accuracy of an automated machine-learning method using smartphone camera-enabled capture and analysis of single, two-dimensional (2D) standing lateral digital images to estimate fat mass (FM) compared to dual X-ray absorptiometry (DXA) in females and males. We also report the first model to predict abdominal FM using 2D digital images. Results: Gender-specific 2D estimates of FM were significantly correlated (p < 0.001) with DXA FM values and not different (p > 0.05). Reproducibility of FM estimates was very high (R2 = 0.99) with high concordance (R2 = 0.99) and low absolute pure error (0.114 to 0.116 kg) and percent error (1.3 and 3%). Bland–Altman plots revealed no proportional bias with limits of agreement of 4.9 to −4.3 kg and 3.9 to −4.9 kg for females and males, respectively. A novel 2D model to estimate abdominal (lumbar 2–5) FM produced high correlations (R2 = 0.99) and concordance (R2 = 0.99) compared to DXA abdominal FM values. Conclusions: A smartphone camera trained with machine learning and automated processing of 2D lateral standing digital images is an objective and valid method to estimate FM and, with proof of concept, to determine abdominal FM. It can facilitate practical identification of the obesity phenotype in adults.

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Gian Luca Farina , Fabrizio Spataro , Antonino De Lorenzo and Henry Lukaski.
PubMed: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191142/
Abstract:
Personal assessments of body phenotype can enhance success in weight management but are limited by the lack of availability of practical methods. We describe a novel smart phone application of digital photography (DP) and determine its validity to estimate fat mass (FM).
This approach utilizes the percent (%) occupancy of an individual lateral whole-body digital image and regions indicative of adipose accumulation associated with increased risk of cardio-metabolic disease. We measured 117 healthy adults (63 females and 54 males aged 19 to 65 years) with DP and dual X-ray absorptiometry (DXA) and report here the development and validation of this application.
Inter-observer variability of the determination of % occupancy was 0.02%. Predicted and reference FM values were significantly related in females (R2 = 0.949, SEE = 2.83) and males (R2 = 0.907, SEE = 2.71).
Differences between predicted and measured FM values were small (0.02 kg, p = 0.96 and 0.07 kg, p = 0.96) for females and males, respectively. No significant bias was found; limits of agreement ranged from 5.6 to 5.4 kg for females and from 5.6 to 5.7 kg for males. These promising results indicate that DP is a practical and valid method for personal body composition assessments.

Jordan R. Moon, Sarah E. Tobkin – Ashley A. Walter – Abbie E. Smith – Chris M. Lockwood – Travis W. Beck – Joel T. Cramer – Jeffrey R. Stout
8 th International Symposium on IN VIVO BODY COMPOSITION STUDIES 9 th -12th July 2008, New York, USA
CONCLUSION: The new DiP-based BV equation produced low SEE and TE values and high r 2 values in both the EX and CON groups and accurately tracked BV changes. Therefore, DiP can be considered a valid method for estimating and tracking BV in men and women

Nicola Di Lorenzo 1 , Michele Servidio, Laura Di Renzo, Carmine Orlandi, Giorgio Coscarella, Achille Gaspari, Antonino De Lorenzo

https://pubmed.ncbi.nlm.nih.gov/16687022/

Abstract

Background: The increasing incidence of obesity and the wider acceptance of laparoscopic surgery, have lead to a 10-fold increase in bariatric operations in the last 10 years. Widely used indices of obesity (weight and body mass index – BMI) cannot adequately distinguish between fat mass (FM), represented by the sum of kilograms (kg) of lipid, and fat-free mass (FFM), inclusive of lean (kg of proteins), bone (kg of minerals), glycogen, and total body water (TBW), which are important parameters for clinical and physiological studies.

Type: Grant
Filed: March 1, 2016
Date of Patent: October 29, 2019
Inventor: Antonio Talluri