Sensitivity and specificity of machine learning and deep learning algorithms in the diagnosis of thoracolumbar injuries resulting in vertebral fractures: A systematic review and meta-analysis

Introduction

Clinicians encounter challenges in promptly diagnosing thoracolumbar injuries (TLIs) and fractures (VFs), motivating the exploration of Artificial Intelligence (AI) and Machine Learning (ML) and Deep Learning (DL) technologies to enhance diagnostic capabilities. Despite varying evidence, the noteworthy transformative potential of AI in healthcare, leveraging insights from daily healthcare data, persists.

Research question

This review investigates the utilization of ML and DL in TLIs causing VFs.

Materials and methods

Employing Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) methodology, a systematic review was conducted in PubMed and Scopus databases, identifying 793 studies. Seventeen were included in the systematic review, and 11 in the meta-analysis. Variables considered encompassed publication years, geographical location, study design, total participants (14,524), gender distribution, ML or DL methods, specific pathology, diagnostic modality, test analysis variables, validation details, and key study conclusions. Meta-analysis assessed specificity, sensitivity, and conducted hierarchical summary receiver operating characteristic curve (HSROC) analysis.

Results

Predominantly conducted in China (29.41%), the studies involved 14,524 participants. In the analysis, 11.76% (N = 2) focused on ML, while 88.24% (N = 15) were dedicated to deep DL. Meta-analysis revealed a sensitivity of 0.91 (95% CI = 0.86-0.95), consistent specificity of 0.90 (95% CI = 0.86-0.93), with a false positive rate of 0.097 (95% CI = 0.068-0.137).

Conclusion

The study underscores consistent specificity and sensitivity estimates, affirming the diagnostic test's robustness. However, the broader context of ML applications in TLIs emphasizes the critical need for standardization in methodologies to enhance clinical utility.

Breaking habits: safety and efficacy of elective electrocardioversion of atrial fibrillation and atrial flutter in the setting of day hospital

Aim To examine safety and efficiency of electrocardioversion (EC) in elective treatment of atrial fibrillation and atrial flutter in the setting of Day Hospital by determining success rate, frequency of adverse events and possible cost benefit compared to admitting a patient into hospital. Methods This prospective observational cohort study was performed in Day Hospital and in Intensive Care Department of Internal Medicine Clinic, University Clinical Centre Tuzla from January 2019 to December 2022 and included 98 patients with a persistent form of atrial fibrillation (AF) or atrial flutter. The patients who were divided in two groups, 56 hospitalized and 42 patients accessed in Day Hospital. In all patients, medical history, physical examination, electrocardiogram (ECG) and transthoracic echocardiogram (TTE) evaluation was performed in addition to laboratory findings. Electrocardioversion was performed with a monophasic General Electric defibrillator in anterolateral electrode position with up to three repetitive shocks. Results In hospital setting group overall succes rate of electrocardioversion was 85%, with average 2.1 EC attemps, there was with one fatal outcome due to stroke, one case of ventricular fibrillation (VF) due to human error, and 6 minor adverse events; with average cost of was 1408.70 KM (720.23 €) per patient. In Day Hospital setting succes rate was 88%, with average 2 EC attempts, no major adverse events, 8 minor adverse events; and average cost was of 127.23 KM (65.05 €) per patient. Conclusion Performing elective electrocardioversion in Day Hospital setting is as safe as admitting patients into hospital but substantially more cost effective.