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Applications of AI in Medical Science and Drug Development
Published in Mark Chang, Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare, 2020
The majority of deep learning is used in imaging analysis, followed by electrodiagnosis, genetic diagnosis, and clinical laboratory testing. In medical applications, the commonly used deep learning algorithms include CNNs, RNNs, and DBNs. CNNs have been recently implemented in the medical area to assist disease diagnosis. For instance, Gulshan et al. (2016) applied a CNN to detect referable diabetic retinopathy through retinal fundus photographs. The sensitivity and specificity of the algorithm are both over 90%, which demonstrates the effectiveness of the technique in the diagnosis of diabetes. Long et al. (2017) used CNN architecture to diagnose congenital cataract disease through learning ocular images with 90% accuracy in diagnosis and treatment recommendations. Esteva et al. (2017) identify skin cancer from clinical images using CNN deep learning. Their method provides over 90% sensitivity and specificity in the diagnosis of malignant lesions and benign lesions.
Impact of AI, IoT and Big Data Analytics in Diseases Diagnosis and Prediction
Published in Pushpa Singh, Divya Mishra, Kirti Seth, Transformation in Healthcare with Emerging Technologies, 2022
Ambrish Kumar Sharma, Apoorva Joshi
Despite the fact that AI research in healthcarҽ is developing, much of it is still focused on a diseasҽ that is not avoidable. We will go over a few examples below:Carcinoma: In a double-blinded validation analysis, İBM Watson for onсology, as proved by Şomashekhar et al., would be a reliable Aİ tool for helping cancer dìagnosis. Clinical images were analyzed by Esteva et al. (2017) to classify skin cancer subtypes.Neurology: Bouton et al. (2016) developed an AI system to help quadriplegic patients regain control of their movements. Ƒarina et al. (2017) looked at the efficiency of an offline man/machìne interface that uses the discharge timings of spinal motor neurons to control upper-lìmb prosthesis.Cardiology: Ɗilsizian and Ṣiegel et al. (2014) spoke about how the AI system could be used to diagnose heart disease using cardiac images.Early detection is critical because early treatment for all three illnesses is necessary to prevent patients’ health from deteriorating, and all are primary causes of death. Furthermore, by enhancing research methods, genetics, etc., which is the AI system’s ability, early diagnosis may be possible. Aİ has been used in the past to treat a variety of other ailments. Two recent examples are L̇ong et al. (2017), who used ocular image evidence to diagnose congenital cataract disease, and Ǵulshan et al. (2016), who employed retinal fundus images to identify referable diabetic retinopathy.
The Use of Artificial Intelligence-Based Models for Biomedical Application
Published in Mohan Lal Kolhe, Kailash J. Karande, Sampat G. Deshmukh, Artificial Intelligence, Internet of Things (IoT) and Smart Materials for Energy Applications, 2023
Sharad Mulik, Nilesh Dhobale, Kanchan Pujari, Kailash Karande
In 2014 at the United States of America, 5.4 million OCT has been scanned. The AI-based DL approach is used to detect eye problems. The eye problem includes age-based macular degeneration, diabetic retinopathy, and glaucoma. The AI is a properly recognized eye problem with 94.50% accuracy through OCT scans [56]. To treat congenital cataracts, the Chinese researcher has developed a CNN-based AI system to identify, estimate, and advise treatment for this disease. This system provides the best results for diagnosing congenital cataract disease, but sometimes its misdiagnosis [64].
A perspective of contemporary cataract surgery: the most common surgical procedure in the world
Published in Journal of the Royal Society of New Zealand, 2020
Charles N. J. McGhee, Jie Zhang, Dipika V. Patel
Congenital cataract may be detected by the presence of an abnormal ocular red reflex. Routine Well Child/Tamariki Ora recommends that red reflex screening is performed within 7 days of birth and at 6 weeks of age by the lead maternity carer (midwife, obstetrician, or general practitioner) or a paediatrician (Ministry of Health 2014). In 2002, a questionnaire study was performed to assess the coverage and quality of routine red reflex screening in the Nelson–Tasman region in the context of New Zealand guidelines (Fry and Wilson 2005). Remarkably, the study reported that 20% of health practitioners involved in infant care in the Nelson–Tasman region were not screening for the red reflex as per the nationally recommended Ministry of Health policy. It was also reported that 47% of midwives and 18% of doctors showed a poor understanding of the importance of such red reflex screening.
Genetics of congenital cataract, its diagnosis and therapeutics
Published in Egyptian Journal of Basic and Applied Sciences, 2018
Luqman Khan, Nargis Shaheen, Qaisar Hanif, Shah Fahad, Muhammad Usman
The human eye is an organ of vision. It plays a major role in life, gives us the sense of sight, and allows to understand about the world around us. Visualization and interpretation of colours, shapes and dimensions of numerous objects are made possible by eye. Inherited eye diseases comprise 1/3 of all reported human genetic disorders [1]. Congenital cataract is the type of cataract, which occurs at the early stages of life [2]. It is described as an opacity of the crystallin lens causing an impaired vision [3], and the abnormality of the lens can impede with the normal development of the eyes [4]. The prevalence varies with socioeconomic rank affecting 1–6 cases out of 10,000 live births in the developed countries and about 5–15 out of 10,000 cases in the developing countries of the world. Autosomal dominant, autosomal recessive and x-linked genetic type of congenital cataracts, which may be sequestered (nonsyndromic) or associated with systemic disorder or syndromes [5]. The phenotypic arrangement is founded over the site & the type of the lens imperviousness that containing posterior polar, anterior polar, lamellar, and nuclear, coralliform, and cerulean, pulverulent, cortical, polymorphic and total cataract [6]. Phenotypically same cataracts might be caused by mutation at different genomic loci and possibly control, unlike inheritance pattern, whereas physically variable cataracts might be seen in a particularly large population [7]. About 50% of cases have a genetic root with other causes counting intrauterine infection, malnourishment and metabolic diseases [3]. Mutation in more than 30 genes is identified to cause non-syndromic forms of innate cataracts [8]. The discovery of the mutations affecting congenital cataract should result in a better way of the procedure concerned in cataractogenesis and give further insights into the normal lens development and structure [9]. Hereditary research has recognised mutations in various genes associated with cataracts like crystalline, which consist of about half of the recognised genetic types of the cataract. Congenital cataracts are either unilateral or bilateral, they are categorised by distinct genetic basis, morphology, the existence of particular metabolic disorders, or linked with other optical abnormalities. Crystallin is concerned about half of the families with recognised mutations [10]. Crystallin compactness and order is critical to the correctness of the lens [2]. Mutations in crystallin that are severe enough to cause accumulation that can lead to congenital cataracts in an exceptionally penetrant Mendelian genetics. In this review, we tried to discuss the description of genetic heterogeneity of congenital cataract disorder and also we shed some light on diagnosis and therapeutics.