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Fulltext Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review

Dabbagh R, Jamal A, Bhuiyan Masud JH, Titi MA, Amer YS, Khayat A, et al.

Cureus, 2023 May;15(5):e38373.
PMID: 37265897 DOI: 10.7759/cureus.38373

During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.
Normabaric Hyperoxia Treatment Improved Locomotor Activity of C57BL/6J Mice through Enhancing Dopamine Genes Following Fluid-Percussion Injury in Striatum

Muthuraju S, Taha S, Pati S, Rafique M, Jaafar H, Abdullah JM

Int J Biomed Sci, 2013 Dec;9(4):194-204.
PMID: 24711754

Closed traumatic brain injury (CTBI) leads to increase mortality rates in developing countries. However, a sustainable therapeutic approach has not been established yet. Therefore, the present study was designed to evaluate the impact of normabaric hyperoxia treatment (NBOT) on striatum associated Locomotor Activity (LA) in IntelliCage after Fluid-Percussion Injury (FPI). Animals were divided in four groups: Group I control (n=24), Group II sham (n=24), Group III FPI (n=24) and Group IV FPI with NBOT (n=24). Animals were habituated in IntelliCage for 4 days following transponder implanted in mice neck region on day 5. Then the LA of all groups was assessed 6hr daily for 5 days before inducing FPI. On day 6, cannula was implanted on the striatum, on day 7 FPI was performed in Group III (kept in normal environment) and IV (immediately exposed to NBOT for 3 hr). LA (in terms of number of visits in all four corners) was assessed 6 hr at days 1, 7, 14, 21 and 28 following FPI. After the animals were sacrificed to study the neuronal damage, dopamine receptors and transporters expression in striatum. The results suggested that the LA of FPI impaired mice as compared to the control and sham showed less number of visits in all four corners in IntelliCage. Morphological results revealed that FPI induced neuronal damage as compared to sham and control. Dopamine receptors and transporters were down regulated in the FPI group as compared to the control. Immediate exposure to NBOT improved LA in terms of increased number of visits in all four corners, reduced number of cell death and improved receptor expression as compared to FPI. In conclusion, NBOT exposure could improve the LA of mice following FPI through prevention of neuronal damage, improved dopamine receptors and transporters.
An international Delphi consensus on patient preparation for metabolic and bariatric surgery

Clyde DR, Adib R, Baig S, Bhasker AG, Byrne J, Cameron D, et al.

Clin Obes, 2025 Apr;15(2):e12722.
PMID: 39673462 DOI: 10.1111/cob.12722

Global obesity rates have risen dramatically, now exceeding deaths from starvation. Metabolic and bariatric surgery (MBS), initially for severe obesity (BMI ≥35 kg/m2), is performed globally over 500 000 times annually, offering significant metabolic benefits beyond weight loss. However, varying eligibility criteria globally impact patient care and healthcare resources. Updated in 2022, ASMBS and IFSO guidelines aim to standardise MBS indications, reflecting current understanding and emphasising comprehensive preoperative assessments. Yet, clinical variability persists, necessitating consensus-based recommendations. This modified Delphi study engaged 45 global experts to establish consensus on perioperative management in MBS. Experts selected from bariatric societies possessed expertise in MBS and participated in a two-round Delphi protocol. Consensus was achieved on 90 of 169 statements (53.3%), encompassing multidisciplinary team composition, patient selection criteria, preoperative testing, and referral pathways. The agreement highlighted the critical role of comprehensive preoperative assessments and the integration of healthcare professionals in MBS. These findings offer essential insights to standardise perioperative practices and advocate for evidence-based guidelines in MBS globally. The study underscores the need for unified protocols to optimise outcomes and guide future research in MBS.
Metabolic and Bariatric Surgeon Criteria-An International Experts' Consensus

Kermansaravi M, Chiappetta S, Shikora SA, Musella M, Kow L, Aarts E, et al.

Obes Surg, 2024 Jul 24.
PMID: 39046625 DOI: 10.1007/s11695-024-07395-y

PURPOSE: With the global epidemic of obesity, the importance of metabolic and bariatric surgery (MBS) is greater than ever before. Performing these surgeries requires academic training and the completion of a dedicated fellowship training program. This study aimed to develop guidelines based on expert consensus using a modified Delphi method to create the criteria for metabolic and bariatric surgeons that must be mastered before obtaining privileges to perform MBS.
METHODS: Eighty-nine recognized MBS surgeons from 42 countries participated in the Modified Delphi consensus to vote on 30 statements in two rounds. An agreement/disagreement among ≥ 70.0% of the experts was regarded to indicate a consensus.
RESULTS: Consensus was reached on 29 out of 30 statements. Most experts agreed that before getting privileges to perform MBS, surgeons must hold a general surgery degree and complete or have completed a dedicated fellowship training program. The experts agreed that the learning curves for the various operative procedures are approximately 25-50 operations for the LSG, 50-75 for the OAGB, and 75-100 for the RYGB. 93.1% of experts agreed that MBS surgeons should diligently record patients' data in their National or Global database.
CONCLUSION: MBS surgeons should have a degree in general surgery and have been enrolled in a dedicated fellowship training program with a structured curriculum. The learning curve of MBS procedures is procedure dependent. MBS surgeons must demonstrate proficiency in managing postoperative complications, collaborate within a multidisciplinary team, commit to a minimum 2-year patient follow-up, and actively engage in national and international MBS societies.