Analysis of food recognition and calorie estimation using AI

Kalivaraprasad, B.; Prasad, M. V. D.; Vamsi, R.; Tejasri, U.; Santhoshi, M. Nvvsld; PramodKumar, Aylapogu

doi:10.1063/5.0074506

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…CNN, or convolutional neural network, is a deep learning method that has been widely applied to image data [ 12 ]. In the case of image classification, the CNN method has succeeded in surpassing machine learning methods such as the SVM method, and currently has the most significant results in image recognition because CNN has a way of working that resembles the function of the human brain, where the computer will be given image data to study [ 19 ], trained to recognize each visual element on the image, and understand each image pattern, so that the computer will be able to identify the image [ 20 ]. CNN has recently advanced to become a sophisticated technique for image classification tasks [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control

Salaki

Shin

Lee

et al. 2023

Sensors

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The management of type 2 diabetes mellitus (T2DM) is generally not only focused on pharmacological therapy. Medical nutrition therapy is often forgotten by patients for several reasons, such as difficulty determining the right nutritional pattern for themselves, regulating their daily nutritional patterns, or even not heeding nutritional diet recommendations given by doctors. Management of nutritional therapy is one of the important efforts that can be made by diabetic patients to prevent an increase in the complexity of the disease. Setting a diet with proper nutrition will help patients manage a healthy diet. The development of Smart Plate Health to Eat is a technological innovation that helps patients and users know the type of food, weight, and nutrients contained in certain foods. This study involved 50 types of food with a total of 30,800 foods using the YOLOv5s algorithm, where the identification, measurement of weight, and nutrition of food were investigated using a Chenbo load cell weight sensor (1 kg), an HX711 weight weighing A/D module pressure sensor, and an IMX219-160 camera module (waveshare). The results of this study showed good identification accuracy in the analysis of four types of food: rice (58%), braised quail eggs in soy sauce (60%), spicy beef soup (62%), and dried radish (31%), with accuracy for weight and nutrition (100%).

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Section: Introductionmentioning

confidence: 99%

Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control

Salaki

Shin

Lee

et al. 2023

Sensors

View full text Add to dashboard Cite

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Sodium Intake Estimation in Hospital Patients Using AI-Based Imaging: Prospective Pilot Study

Ryu,

Kim,

Lim

et al. 2024

JMIR Form Res

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Background Measurement of sodium intake in hospitalized patients is critical for their care. In this study, artificial intelligence (AI)–based imaging was performed to determine sodium intake in these patients. Objective The applicability of a diet management system was evaluated using AI-based imaging to assess the sodium content of diets prescribed for hospitalized patients. Methods Based on the information on the already investigated nutrients and quantity of food, consumed sodium was analyzed through photographs obtained before and after a meal. We used a hybrid model that first leveraged the capabilities of the You Only Look Once, version 4 (YOLOv4) architecture for the detection of food and dish areas in images. Following this initial detection, 2 distinct approaches were adopted for further classification: a custom ResNet-101 model and a hyperspectral imaging-based technique. These methodologies focused on accurate classification and estimation of the food quantity and sodium amount, respectively. The 24-hour urine sodium (UNa) value was measured as a reference for evaluating the sodium intake. Results Results were analyzed using complete data from 25 participants out of the total 54 enrolled individuals. The median sodium intake calculated by the AI algorithm (AI-Na) was determined to be 2022.7 mg per day/person (adjusted by administered fluids). A significant correlation was observed between AI-Na and 24-hour UNa, while there was a notable disparity between them. A regression analysis, considering patient characteristics (eg, gender, age, renal function, the use of diuretics, and administered fluids) yielded a formula accounting for the interaction between AI-Na and 24-hour UNa. Consequently, it was concluded that AI-Na holds clinical significance in estimating salt intake for hospitalized patients using images without the need for 24-hour UNa measurements. The degree of correlation between AI-Na and 24-hour UNa was found to vary depending on the use of diuretics. Conclusions This study highlights the potential of AI-based imaging for determining sodium intake in hospitalized patients.

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Sodium Intake Estimation in Hospital Patients Using AI-Based Imaging: Prospective Pilot Study (Preprint)

Ryu,

Kim,

Lim

et al. 2023

Preprint

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BACKGROUND Measurement of sodium intake in hospitalized patients is critical for their care. In this study, artificial intelligence (AI)-based imaging was performed to determine sodium intake in these patients. OBJECTIVE The applicability of a diet management system was evaluated using AI-based imaging to assess the sodium content of diets prescribed for hospitalized patients. METHODS Based on the information on the already investigated nutrients and quantity of food, consumed sodium was analyzed through photographs obtained before and after a meal. You only look once (YOLO v4)-based models and convolutional neural networks, including ResNet-101, were used to classify food and dish areas as well as food quantity, respectively. The 24-h urine sodium (UNa) value was measured as a reference for evaluating the sodium intake. RESULTS Among the 54 people enrolled, 25 participants with full data were analyzed. The results revealed that the median sodium intake calculated by the AI algorithm (AI-Na) was 2022.7 mg per day/person (adjusted by administered fluids). Although the 24-h UNa revealed a significant relationship with AI-Na along with the estimated glomerular filtration rate, the AI-Na calculations and 24-h UNa measurements differed considerably. Finally, a formula was derived using regression with an interaction term considering patients’ characteristics, such as sex, age, renal function, the use of diuretics, and administered fluids; thus, AI-Na has clinical significance in the calculation of salt intake in hospitalized patients using images without measuring 24-h UNa. Furthermore, we estimated that AI-Na corresponds to the 24-h UNa, dependent on a factor of 2.355 in the diuretics group and 0.353 in the non-diuretics group, indicating that the use of diuretics affects sodium excretion. CONCLUSIONS This study highlights the potential of AI-based imaging for determining sodium intake in hospitalized patients.

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Analysis of food recognition and calorie estimation using AI

Cited by 3 publications

References 9 publications

Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control

Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control

Sodium Intake Estimation in Hospital Patients Using AI-Based Imaging: Prospective Pilot Study

Sodium Intake Estimation in Hospital Patients Using AI-Based Imaging: Prospective Pilot Study (Preprint)

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