A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges

Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis, which is a chronic disease affecting young to aged population. This paper provides a survey of recent and the most representative deep learning techniques (published between 2018 to 2020) for the diagnosis of osteoarthritis and rheumatoid arthritis. The paper also reviews traditional machine learning methods (published 2015 onward) and their application for the diagnosis of these diseases. The paper identifies open problems and research gaps. We believe that deep learning can assist general practitioners and consultants to predict the course of the disease, make treatment propositions and appraise their potential benefits

Novel deep learning approach to model and predict the spread of COVID-19

SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally, producing new variants and has become a pandemic. People have lost their lives not only due to the virus but also because of the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop robust artificial intelligence techniques to predict the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models are trained and tested on publicly available novel coronavirus dataset. The proposed models are evaluated by using Mean Absolute Error and compared with the existing methods for the prediction of the spread of COVID-19. Our experimental results demonstrate the superior prediction performance of the proposed models. The proposed DSPM and NRM achieve MAEs of 388.43 (error rate 1.6%) and 142.23 (0.6%), respectively compared to 6508.22 (27%) achieved by baseline SVM, 891.13 (9.2%) by Time-Series Model (TSM), 615.25 (7.4%) by LSTM-based Data-Driven Estimation Method (DDEM) and 929.72 (8.1%) by Maximum-Hasting Estimation Method (MHEM)

Novel and recurrent LDLR gene mutations in Pakistani hypercholesterolemia patients

Item does not contain fulltextThe majority of patients with the autosomal dominant disorder familial hypercholesterolemia (FH) carry novel mutations in the low density lipoprotein receptor (LDLR) that is involved in cholesterol regulation. In different populations the spectrum of mutations identified is quite different and to date there have been only a few reports of the spectrum of mutations in FH patients from Pakistan. In order to identify the causative LDLR variants the gene was sequenced in a Pakistani FH family, while high resolution melting analysis followed by sequencing was performed in a panel of 27 unrelated sporadic hypercholesterolemia patients. In the family a novel missense variant (c.1916T > G, p.(V639G)) in exon 13 of LDLR was identified in the proband. The segregation of the identified nucleotide change in the family and carrier status screening in a group of 100 healthy subjects was done using restriction fragment length polymorphism analysis. All affected members of the FH family carried the variant and none of the non-affected members nor any of the healthy subjects. In one of the sporadic cases, two sequence changes were detected in exon 9, one of these was a recurrent missense variant (c.1211C > T; p.T404I), while the other was a novel substitution mutation (c.1214 A > C; N405T). In order to define the allelic status of this double heterozygous individual, PCR amplified fragments were cloned and sequenced, which identified that both changes occurred on the same allele. In silico tools (PolyPhen and SIFT) were used to predict the effect of the variants on the protein structure, which predicted both of these variants to have deleterious effect. These findings support the view that there will be a novel spectrum of mutations causing FH in patients with hypercholesterolaemia from Pakistan