scholarly journals goFOODTM: An Artificial Intelligence System for Dietary Assessment

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4283 ◽  
Author(s):  
Ya Lu ◽  
Thomai Stathopoulou ◽  
Maria F. Vasiloglou ◽  
Lillian F. Pinault ◽  
Colleen Kiley ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fast Food ◽  
Dietary Assessment ◽  
Reconstruction Algorithm ◽  
Multimedia Databases ◽  
Assessment System ◽  
Accurate Estimation ◽  
Food Category ◽  
Intelligence System ◽  
Macronutrient Content

Accurate estimation of nutritional information may lead to healthier diets and better clinical outcomes. We propose a dietary assessment system based on artificial intelligence (AI), named goFOODTM. The system can estimate the calorie and macronutrient content of a meal, on the sole basis of food images captured by a smartphone. goFOODTM requires an input of two meal images or a short video. For conventional single-camera smartphones, the images must be captured from two different viewing angles; smartphones equipped with two rear cameras require only a single press of the shutter button. The deep neural networks are used to process the two images and implements food detection, segmentation and recognition, while a 3D reconstruction algorithm estimates the food’s volume. Each meal’s calorie and macronutrient content is calculated from the food category, volume and the nutrient database. goFOODTM supports 319 fine-grained food categories, and has been validated on two multimedia databases that contain non-standardized and fast food meals. The experimental results demonstrate that goFOODTM performed better than experienced dietitians on the non-standardized meal database, and was comparable to them on the fast food database. goFOODTM provides a simple and efficient solution to the end-user for dietary assessment.

scholarly journals Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients

Nutrients ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 4539
Author(s):  
Ioannis Papathanail ◽  
Jana Brühlmann ◽  
Maria F. Vasiloglou ◽  
Thomai Stathopoulou ◽  
Aristomenis K. Exadaktylos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Positive Impact ◽  
Standard Procedure ◽  
Dietary Assessment ◽  
Fine Tuning ◽  
Macronutrient Intake ◽  
Intelligence System ◽  
Clinical Procedures ◽  
Hospitalised Patients ◽  
Before And After

Malnutrition is common, especially among older, hospitalised patients, and is associated with higher mortality, longer hospitalisation stays, infections, and loss of muscle mass. It is therefore of utmost importance to employ a proper method for dietary assessment that can be used for the identification and management of malnourished hospitalised patients. In this study, we propose an automated Artificial Intelligence (AI)-based system that receives input images of the meals before and after their consumption and is able to estimate the patient’s energy, carbohydrate, protein, fat, and fatty acids intake. The system jointly segments the images into the different food components and plate types, estimates the volume of each component before and after consumption, and calculates the energy and macronutrient intake for every meal, based on the kitchen’s menu database. Data acquired from an acute geriatric hospital as well as from our previous study were used for the fine-tuning and evaluation of the system. The results from both our system and the hospital’s standard procedure were compared to the estimations of experts. Agreement was better with the system, suggesting that it has the potential to replace standard clinical procedures with a positive impact on time spent directly with the patients.

scholarly journals Artificial Intelligence-Based Assessment System for Evaluating Suitable Range of Heel Height

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 38374-38385
Author(s):  
Si-Huei Lee ◽  
Bor-Shing Lin ◽  
Hsiang-Chen Lee ◽  
Xiao-Wei Huang ◽  
Ya-Chu Chi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Assessment System ◽  
Heel Height

330 DEVELOPMENT AND VALIDATION OF AN ARTIFICIAL INTELLIGENCE SYSTEM TO OPTIMIZE CLINICIAN REVIEW OF PATIENT RECORDS

2021 ◽  
Vol 160 (6) ◽  
pp. S-64-S-65
Author(s):  
Ethan A. Chi ◽  
Gordon Chi ◽  
Cheuk To Tsui ◽  
Yan Jiang ◽  
Karolin Jarr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Patient Records ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Development And Validation

scholarly journals COVID-19-induced vascular angiopathy: CTPA signs in critically ill patients other than acute pulmonary embolism and high-lung opacity scores

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Mohamed Hossameldin khalifa ◽  
Ahmed Samir ◽  
Ayman Ibrahim Baess ◽  
Sara Samy Hendawi
Keyword(s):  
Artificial Intelligence ◽  
Pulmonary Embolism ◽  
Lung Volume ◽  
Acute Pulmonary Embolism ◽  
Pulmonary Angiography ◽  
Vascular Tree ◽  
Ct Pulmonary Angiography ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Severity Scores

Abstract Background Vascular angiopathy is suggested to be the major cause of silent hypoxia among COVID-19 patients without severe parenchymal involvement. However, pulmonologists and clinicians in intensive care units become confused when they encounter acute respiratory deterioration with neither severe parenchymal lung involvement nor acute pulmonary embolism. Other radiological vascular signs might solve this confusion. This study investigated other indirect vascular angiopathy signs on CT pulmonary angiography (CTPA) and involved a novel statistical analysis that was performed to determine the significance of associations between these signs and the CT opacity score of the pathological lung volume, which is calculated by an artificial intelligence system. Results The study was conducted retrospectively, during September and October 2020, on 73 patients with critical COVID-19 who were admitted to the ICU with progressive dyspnea and low O2 saturation on room air (PaO2 < 93%). They included 53 males and 20 females (73%:27%), and their age ranged from 18 to 88 years (mean ± SD=53.3 ± 13.5). CT-pulmonary angiography was performed for all patients, and an artificial intelligence system was utilized to quantitatively assess the diseased lung volume. The radiological data were analyzed by three expert consultant radiologists to reach consensus. A low CT opacity score (≤10) was found in 18 patients (24.7%), while a high CT opacity score (>10) was found in 55 patients (75.3%). Pulmonary embolism was found in 24 patients (32.9%); three of them had low CT opacity scores. Four other indirect vasculopathy CTPA signs were identified: (1) pulmonary vascular enlargement (57 patients—78.1%), (2) pulmonary hypertension (14 patients—19.2%), (3) vascular tree-in-bud pattern (10 patients—13.7%), and (4) pulmonary infarction (three patients—4.1%). There were no significant associations between these signs and the CT opacity score (0.3205–0.7551, all >0.05). Furthermore, both pulmonary vascular enlargement and the vascular tree-in-bud sign were found in patients without pulmonary embolism and low CT-severity scores (13/15–86.7% and 2/15–13.3%, respectively). Conclusion Pulmonary vascular enlargement or, less commonly, vascular tree-in-bud pattern are both indirect vascular angiopathy signs on CTPA that can explain the respiratory deterioration which complicates COVID-19 in the absence of severe parenchymal involvement or acute pulmonary embolism.

Sign in / Sign up

Export Citation Format

Share Document