The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The purpose of this study is to compute the risk of bias (RoB) automatically. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. It is subjective and does not have a good prognosis. The online version contains supplementary material available at 10.1007/s10466-x.īackground and Motivation: Diagnosis of Parkinson's disease (PD) is often based on medical attention and clinical signs. (b) AI will provide a powerful impetus to the HAR industry in future. We conclude that: (a) HAR industry will evolve in terms of the three pillars of electronic devices, applications and the type of AI. Further, these trained models need solid prediction, high accuracy, generalization, and finally, meeting the objectives of the applications without bias (4) little work was observed in abnormality detection during actions and (5) almost no work has been done in forecasting actions. Five major findings were: (1) HAR constitutes three major pillars such as devices, AI and applications (2) HAR has dominated the healthcare industry (3) Hybrid AI models are in their infancy stage and needs considerable work for providing the stable and reliable design. Further, the review presents the recommendations for an improved HAR design, its reliability, and stability. In our proposed review, a detailed narration on the three pillars of HAR is presented covering the period from 2011 to 2021. There are many review articles published on the general characteristics of HAR, a few have compared all the HAR devices at the same time, and few have explored the impact of evolving AI architecture. This yields a better understanding of rapidly growing acquisition devices, AI, and applications, the three pillars of HAR under one roof.
While electronic devices and their applications are steadily growing, the advances in Artificial intelligence (AI) have revolutionized the ability to extract deep hidden information for accurate detection and its interpretation. Human activity recognition (HAR) has multifaceted applications due to its worldly usage of acquisition devices such as smartphones, video cameras, and its ability to capture human activity data.
The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. Two trained, blinded senior radiologists conducted ground truth annotations. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. As the first study of its kind, “COVLIAS 1.0-Unseen” proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations.
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