The development of machine learning in bariatric surgery

BackgroundMachine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the medical domain. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery.MethodsThe study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR). A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. Eligible studies included journals published from 2016 to the current date. The PRESS checklist was used to evaluate the consistency demonstrated during the process.ResultsA total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML algorithms in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation.ConclusionsThis study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. The evidence suggests that bariatric surgeons can benefit from ML algorithms since they will facilitate the prediction and evaluation of patient outcomes. Also, ML approaches to enhance work processes by making data categorization and analysis easier. However, further large multicenter studies are required to validate results internally and externally as well as explore and address limitations of ML application in bariatric surgery

Water-pipe smoking and serum testosterone levels in adult males in Qatar.

Water-pipe (WP) smoking is the most common method of tobacco consumption in the Middle-East and is rapidly spreading on a global scale. Although, water-pipe smoking is linked to various diseases, such as emphysema and various types of cancers, its effect on testosterone levels has yet to be investigated. This study explores the effect of water-pipe smoking on serum testosterone levels in males in Qatar. In this cross-sectional sample within a cohort study, we retrieved data for a total of 1000 male volunteers from the Qatar BioBank (QBB) project. A self-reported questionnaire was used to determine the water-pipe smoking status of participants. Moreover, participants were stratified based on the frequency of smoking. Total testosterone and sex hormone binding globulin (SHBG) were measured clinically, whereas free testosterone and bioavailable testosterone were calculated using Vermeulen's equation. Hormone values of 541 males (277 water-pipe smokers and 264 non-smokers) were compared using multiple regression analysis based on water-pipe smoking status after adjusting for confounding factors. No statistically significant difference was observed between WP smokers and non-water-pipe smokers in the likelihood of having lower or higher total testosterone, after adjustment for confounding factors. Similar results were found in free testosterone, bioavailable testosterone, and sex hormone binding globulin (all p>0.05). When compared with the reference group, both light and heavy water-pipe smokers had a similar likelihood of circulating low total testosterone levels (OR=0.83, 95% CI: 0.46-1.49; and OR=0.80, 95% CI: 0.43-1.49; respectively). Our results reveal, for the first time, that there is no significant change in total testosterone, free testosterone, bioavailable testosterone and sex hormone binding globulin in waterpipe smokers compared to non-water-pipe smokers. Therefore, we believe that further studies are needed to confirm the effect of water-pipe smoking on testosterone in different populations.This work was supported by the College of Medicine of Qatar University and grant QUST-2-CMED-2018-1 from Qatar University

Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.COVID19 Emergency Response Grant #QUERG-CENG-2020-1 from Qatar University, Doha, Qatar provided the support for the work and the claims made herein are solely the responsibility of the authors

Quality Assessment of Published Systematic Reviews in High Impact Cardiology Journals: Revisiting the Evidence Pyramid

Objective: Systematic reviews are increasingly used as sources of evidence in clinical cardiology guidelines. In the present study, we aimed to assess the quality of published systematic reviews in high impact cardiology journals.Methods: We searched PubMed for systematic reviews published between 2010 and 2019 in five general cardiology journals with the highest impact factor (according to Clarivate Analytics 2019). We extracted data on eligibility criteria, methodological characteristics, bias assessments, and sources of funding. Further, we assessed the quality of retrieved reviews using the AMSTAR tool.Results: A total of 352 systematic reviews were assessed. The AMSTAR quality score was low or critically low in 71% (95% CI: 65.7–75.4) of the assessed reviews. Sixty-four reviews (18.2%, 95% CI: 14.5–22.6) registered/published their protocol. Only 221 reviews (62.8%, 95% CI: 57.6–67.7) reported adherence to the EQUATOR checklists, 208 reviews (58.4%, 95% CI: 53.9–64.1) assessed the risk of bias in the included studies, and 177 reviews (52.3%, 95% CI: 45.1–55.5) assessed the risk of publication bias in their primary outcome analysis. The primary outcome was statistically significant in 274 (79.6%, 95% CI: 75.1–83.6) and had statistical heterogeneity in 167 (48.5%, 95% CI: 43.3–53.8) reviews. The use and sources of external funding was not disclosed in 87 reviews (24.7%, 95% CI: 20.5–29.5). Data analysis showed that the existence of publication bias was significantly associated with statistical heterogeneity of the primary outcome and that complex design, larger sample size, and higher AMSTAR quality score were associated with higher citation metrics.Conclusion: Our analysis uncovered widespread gaps in conducting and reporting systematic reviews in cardiology. These findings highlight the importance of rigorous editorial and peer review policies in systematic review publishing, as well as education of the investigators and clinicians on the synthesis and interpretation of evidence

The diagnostic accuracy of intraoperative frozen section biopsy for diagnosis of sentinel lymph node metastasis in breast cancer patients: a meta-analysis

: Sentinel lymph node (SLN) sampling is important for evaluating the nodal stage of breast cancer when the axillary nodes are clinically free of metastasis. The intraoperative frozen section (IFS) of SLN is used for lymph node assessment. This meta-analysis aims to provide evidence about the diagnostic accuracy and the applicability of IFS of SLN in breast cancer patients. Data were collected by searching PubMed, Cochrane, Scopus, and Web of Science electronic databases for trials matching our eligibility criteria. The statistical analysis included the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and pooled studies' diagnostic odds ratio outcomes. The analyses were conducted using the Open Meta-analyst software. This meta-analysis pooled the results of 110 studies. The overall sensitivity of IFS for SLN metastasis was 74.7%; 95% CI [72.0, 77.2], P < 0.001. It was 31.4% 95% CI [25.2, 38.3], P < 0.001 for the micro-metastasis, and 90.2%; 95% CI [86.5, 93.0], P < 0.001 for the macro-metastasis. The overall specificity was 99.4%; 95% CI [99.2, 99.6], P < 0.001. The overall positive likelihood ratio was 121.4; 95% CI [87.9, 167.6], P < 0.001, and the overall negative likelihood ratio was 0.226; 95% CI [0.186, 0.274], P < 0.001. The overall diagnostic odds ratio of IFS for diagnosing SLN metastasis was 569.5; 95% CI [404.2, 802.4], P < 0.001. The intraoperative frozen section of SLN has good sensitivity for diagnosing breast cancer macro-metastasis. However, the sensitivity is low for micro-metastasis. The specificity is very satisfactory

Characterizing the morbid genome of ciliopathies

Background Ciliopathies are clinically diverse disorders of the primary cilium. Remarkable progress has been made in understanding the molecular basis of these genetically heterogeneous conditions; however, our knowledge of their morbid genome, pleiotropy, and variable expressivity remains incomplete. Results We applied genomic approaches on a large patient cohort of 371 affected individuals from 265 families, with phenotypes that span the entire ciliopathy spectrum. Likely causal mutations in previously described ciliopathy genes were identified in 85% (225/265) of the families, adding 32 novel alleles. Consistent with a fully penetrant model for these genes, we found no significant difference in their “mutation load” beyond the causal variants between our ciliopathy cohort and a control non-ciliopathy cohort. Genomic analysis of our cohort further identified mutations in a novel morbid gene TXNDC15, encoding a thiol isomerase, based on independent loss of function mutations in individuals with a consistent ciliopathy phenotype (Meckel-Gruber syndrome) and a functional effect of its deficiency on ciliary signaling. Our study also highlighted seven novel candidate genes (TRAPPC3, EXOC3L2, FAM98C, C17orf61, LRRCC1, NEK4, and CELSR2) some of which have established links to ciliogenesis. Finally, we show that the morbid genome of ciliopathies encompasses many founder mutations, the combined carrier frequency of which accounts for a high disease burden in the study population. Conclusions Our study increases our understanding of the morbid genome of ciliopathies. We also provide the strongest evidence, to date, in support of the classical Mendelian inheritance of Bardet-Biedl syndrome and other ciliopathies