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Food Image Recognition Using CNN, Faster R-CNN and YOLO
Published in Sam Goundar, Archana Purwar, Ajmer Singh, Applications of Artificial Intelligence, Big Data and Internet of Things in Sustainable Development, 2023
The quest for a balanced dietary intake is a desire for most of the people in the world today. Everyone wants to eat healthy these days. Dietary habits directly influence the quality of life an individual possesses. Obesity is one of the primary reasons for most chronic diseases. So, to control obesity and to be in shape, one needs to eat healthy and also control the portion size. In this digital era, everyone is dependent on technology for most of their needs, including dietary intake. Technology as a medium could be used to increase awareness amongst people for moving to a healthier life. People are dependent on various mobile food tracking applications and websites for controlling their food intake. Dietary assessment is one crucial thing that needs to be managed accurately and efficiently. Conventional methods are really difficult to use. Nowdays hundreds of applications and softwares are present to monitor diet. While some of them let users manually enter the food type that they are eating, some let the users click a picture to process it. Methods to monitor the diet don’t matter much; what matters is the classification accuracy of food. The system should be able to recognize the food accurately and efficiently.
Revolutionizing Healthcare
Published in Bharat Bhushan, Nitin Rakesh, Yousef Farhaoui, Parma Nand Astya, Bhuvan Unhelkar, Blockchain Technology in Healthcare Applications, 2022
Kavitha Rajamohan, Sangeetha Rangasamy, Surekha Nayak, R. Anuradha, Aarthy Chellasamy
Chronic diseases are not curable and patients have to live with them throughout their life. Chronicle healthcare patients require continuous monitoring, mental and dietary counselling, to avoid escalation of the disease, which may prove fatal. IoMT devices such as blood sugar level monitoring, body weight, bone strength monitoring and electrolyte concentration helps diabetic and arthritis patients to continuously monitor for any anomaly. Ingestible pills help in monitoring medicinal intake by Alzheimer or bipolar disorder patients. Wearable and app-based devices with diet counselling for various chronic illnesses linked with vital parameters will prevent several critical situations. Support group help, counselling, success stories through shared apps, community-based links with medical and pharma support available for chronic conditions are possible through decentralized IoMT.
AI and Chronic Inflammation
Published in Louis J. Catania, AI for Immunology, 2021
Generally speaking, patients with chronic diseases can be somewhat de facto assumed to have some degree of chronic inflammation. The Center for Disease Control (CDC) defines chronic diseases as “conditions that last one year or more and require ongoing medical attention or limit activities of daily living or both.”15 Chronic diseases such as heart disease, cancer, and diabetes also are the leading causes of death and disability in the United States and are the leading drivers of the nation’s $3.5 trillion in annual health care costs. No surprise when considering the prevalence of chronic diseases. Six in ten adults in the United States have a chronic disease. Four in ten have two or more (comorbidities).16 While there are significant numbers of disease states that can be classified as chronic, Table 3.3 lists the ten most common chronic conditions ranked by death rate. Such prioritized lists vary based on demographic factors (i.e., age, gender, race, geographic location, and socioeconomics):17 Notwithstanding such demographic considerations, as mentioned previously, “…chronic inflammatory disease is the progenitor or originating cause of all the major chronic disease categories.”
Injury patterns among national-level athletes in Lebanon: a retrospective study
Published in Research in Sports Medicine, 2022
Lana El Osta, Abdo El Helou, Habib Aimé Hatem, Nada El Osta
1. Socio-demographic characteristics included age in years and gender (male, female). Lifestyle behaviours included tobacco consumption (current, past, never), alcohol consumption (never, less than once a week, 1–3 times a week, regularly), daily fluid intake in litres, daily sleep hours. Health conditions comprised height in cm, weight in kg, Body Mass Index (BMI) in kg/m2, presence of medical conditions (yes, no), and chronic medication intake (yes, no). Chronic medications are taken regularly in the treatment of chronic diseases, such as cancer, cardiovascular disease, asthma, diabetes mellitus, autoimmune disease, thyroid disease (Bernell & Howard, 2016). And participants were asked to report any medications they have been taking for more than 3 months for a chronic medical condition.
A two-phase hybrid approach using feature selection and Adaptive SVM for chronic disease classification
Published in International Journal of Computers and Applications, 2021
Chronic diseases are long-term diseases which are gradually increasing the mortality rate and hindering the economic development of the world. Chronic diseases have a severe impact on the life of people all over the world. Globally, chronic diseases are the dominant cause of death and disability in the world. It has been estimated that by the year 2020, about 150 million people will be affected with chronic diseases in the U.S. and that the clinical overhead for the treatment will be 80% of all the healthcare expenditures [1]. According to the World Health Organization [2], in 2005, about 53% of deaths occurred due to chronic diseases in India. The most prevailing chronic diseases include Cardiac Failure, Human Immunodeficiency Virus (HIV), Diabetes Mellitus, Obesity, Cancer, Haemophilia, Chronic Respiratory Diseases, Strokes, Chronic Kidney Disease (CKD), Hyperlipidaemia, Cardiac Arrhythmia, Coronary Artery Disease, Hypertension, Parkinson’s Disease, Asthma, Arthritis. With the developing technologies for reducing the stride of disease, early identification of persons and treatment to those who are at a greater risk of disease is of utmost importance.
Pesticide handling practices and health risks among the apple orchard workers in Western Indian Himalayan region
Published in Human and Ecological Risk Assessment: An International Journal, 2021
D. Kumari, A. J. Sebastian, S. John
Self-reported health effects: The health effects data was also collected during the interview by the pre-structured questionnaire as mentioned earlier. The response was documented “yes” or “no” and for each response of “yes,” it was further interrogated to delineate the type of acute health symptoms. Statement of each symptoms was weighted on five-point Likert scale ranged from never (= 1) to always (=5). Internal consistency of the scale was calculated by Cronbach’s alpha for acute pesticide poisoning (APP) was 0.78. Acute pesticide poisoning (APP) included the symptoms: eye burning, excessive tearing, burning of nose, red eyes, dizziness, loss of consciousness, headache, vomiting, cough, chest pain, itchy skin, dryness, diarrhea, stomach cramps and feeling exhausted. The chronic diseases data were collected with the response “yes or “no.” The chronic diseases included: asthma, hypertension, diabetes, cancer and others (immune suppression, hormone disruption, and reproductive abnormalities).