Performance Analysis of a Deep Learning Algorithm to Detect MRI Positive Sacroiliac Joints in Patients with Axial Spondyloarthritis According to the Assessment of SpondyloArthritis international Society 2009 Definition

Objectives: To assess the ability of a previously trained deep learning algorithm to identify magnetic resonance imaging positive (MRI+) scans of the sacroiliac joints (SIJ) in a large external validation set of patients with axial spondylarthritis (axSpA).Methods: Baseline SIJ MRI scans were collected from two prospective randomised controlled trials in patients with non-radiographic (nr-) and radiographic (r-) axSpA (NCT01087762 and NCT02505542) and were centrally evaluated by two expert readers (and adjudicator in case of disagreement) for the presence of inflammation by the 2009 Assessment in SpondyloArthritis international Society (ASAS) definition. Scans were processed by the deep learning algorithm, blinded to clinical information and central expert readings

Performance Analysis of a Deep Learning Algorithm to Detect MRI Positive Sacroiliac Joints in Patients with Axial Spondyloarthritis According to the Assessment of SpondyloArthritis international Society 2009 Definition

Objectives: To assess the ability of a previously trained deep learning algorithm to identify magnetic resonance imaging positive (MRI+) scans of the sacroiliac joints (SIJ) in a large external validation set of patients with axial spondylarthritis (axSpA).Methods: Baseline SIJ MRI scans were collected from two prospective randomised controlled trials in patients with non-radiographic (nr-) and radiographic (r-) axSpA (NCT01087762 and NCT02505542) and were centrally evaluated by two expert readers (and adjudicator in case of disagreement) for the presence of inflammation by the 2009 Assessment in SpondyloArthritis international Society (ASAS) definition. Scans were processed by the deep learning algorithm, blinded to clinical information and central expert readings

Linking emergency medical department and road traffic police casualty data: a tool in assessing the burden of injuries in less resourced countries

Objective: The study aimed to (1) assess the magnitude of road traffic injuries in a country missing a formal linkage system of police with hospital data, (2) quantify the underreporting, and (3) produce a convenient algorithm exploring its constituent components. Methods: Linkage of disaggregate (individual) data collected by the road traffic police (RTP) with those by the Emergency Department Injury Surveillance System (EDISS) on the Greek island of Corfu and coded with different classification systems was carried out. The applied four-step methodology, also comprising the calculation of underreporting coefficients of the variation by basic demographic variables, mode of transport, and injury outcome, led to the identification of the overall underreporting from either registry. Results: RTP data captured 96.6% (coefficient: 1.035), whereas EDISS captured only 54.4% of total fatalities (overall concordance: 51.1%). On the contrary, EDISS captured 94.6% of nonfatal injuries, whereas RTP only captured 16% (coefficient: 6.238), resulting in a low overall concordance (10.6%). Considering severity of injury assessed by EDISS, by using the ISS as the gold standard, RTP data misclassified 20.3% of severe injuries as less severe, and a statistically significant difference in the underreporting by gender was also noted. Conclusion: Relatively simple methodologies can provide essential coefficients to assess the actual numbers, severity, and components of road casualties by complementing routinely collected RTP with sentinel emergency department reporting systems