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21st October 2022
The use of a convolutional neural network (CNN) deep learning model significantly improved the diagnostic accuracy of orthopaedic surgeons’ detection of fractures as well as the time taken to make the diagnosis according to a study by Japanese researchers.
One of the main causes of diagnostic errors in the emergency department is the failure to correctly interpret radiographs and that the majority of diagnoses missed on radiographs are fractures. Moreover, in patients with multiple traumas, whole body CT scans are generally the preferred imaging modality, but a study has suggested that on a per-scan basis, there were 12.9% of missed injuries, of which 2.5% were clinically significant. The use of a CNN model for automatic detection of rib fractures detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists’ workload. With some evidence that a CNN model can detect individual fractures, there remains uncertainty over whether such models could be of assistance in reducing missed fractures in a wide range of sites such as the spine, ribs and pelvis.
For the present study, the Japanese team examined whether a fracture detection algorithm could be of assistance to orthopaedic surgeons and compared their diagnostic accuracy with and without the CNN model. The team retrospectively reviewed patients who were diagnosed with either a pelvic, rib or spine fracture and who had undergone a CT scan within 7 days of their trauma. The CNN model was trained, and the performance assessed in terms of sensitivity, precision and the F1-score (which is a measure of the model’s accuracy).
CNN model and orthopaedic surgeon’s diagnostic performance
The training and validation set included 181 patients with a mean age of 54.3 (35.3% female). The testing set included 19 patients (mean age 51.2, 31.5% female) with 2,447 CT images and an average of 129 images per patients. The prevalence of pelvic, rib and spine fractures among the images were 5.8%, 3.6% and 5.5% respectively.
The CNN model itself had an overall mean sensitivity of 78.6%, a precision of 64.8% and an F1-score of 71.1% and also high for the individual fracture regions, e.g., pelvic (83.9%), rib (71.3%) and spine (78%).
Three orthopaedic surgeons with 3, 3 and years of experience respectively, reviewed the CT scans both with and without assistance from the CNN model.
For the detection of fractures of a whole-body CT scan, the mean sensitivity for the first surgeon was 69.7% alone but increased to 82.9% with the CNN model (p < 0.0001) and there were similar and significant differences for the other two surgeons. The model also improved the diagnostic accuracy for each of the three scanned areas.
When the researchers examined the time to diagnose the fracture, i.e., based on reading and interpreting the scan, the first surgeon diagnosed a fracture in 278.4 seconds, and this reduced to 162.3 seconds with assistance from the CNN model (p < 0.0001). Once again, there were significantly shorter diagnostic times for the other two surgeons.
The authors concluded that while their CNN model provided a good sensitivity for the detection of pelvic, rib and spinal fractures, when used by the orthopaedic surgeons their sensitivity improved as did the time to make a fracture diagnosis.
Inoue T et al. Automated fracture screening using an object detection algorithm on whole-body trauma computed tomography Sci Rep 2022
20th July 2021
The use of mitigation strategies for COVID-19 such as social distancing and the wearing of face-masks have helped to minimise the spread of the virus. In addition, the use of an effective Test-Trace-Isolate (TTI) strategy, involving the testing of symptomatic cases and tracing their contacts, is crucial for the detection of the virus. The current gold standard test is the polymerase chain reaction (PCR) although this testing modality can only be undertaken within a hospital laboratory and is therefore expensive for population-wide testing. While the testing process itself takes several hours, there are additional time constraints and which slow the overall process, e.g., transporting of samples to the laboratory testing site, presence of a sufficient number of trained staff and high sample volumes. An alternative is the use of lateral flow tests and these have been recognised as being able to increase testing capacity.
Lateral flow tests are are much cheaper than PCR tests and can be produced in large quantities and deliver results on site and within 15 to 30 minutes. Whereas PCR tests involve amplification of the nucleic acid sample, lateral flow tests rely on the detection of a viral antigen in the patient’s sample and are therefore deemed to be of lower sensitivity. However, there have been no comparative studies of outcomes associated with the use of PCR and antigen testing in the same patient cohort and this led a team from the Centre for Primary Care, Wolfson Institute of Population Health, Queen Mary University, London, to assess the diagnostic accuracy of both methods in the same patients. The study was undertaken among a network of general practitioners in Austria and included patients who self-reported mild to moderate flu-like illness (e.g., cough, fever, runny nose etc). Such individuals received a same-day appointment and given an antigen test and also given a PCR test.
Based on lateral flow tests for 2562 patients, 1027 who tested positive, were also given a PCR test and of whom, 826 (79.7%) tested positive. From this cohort of 826, 788 had also tested positive on the antigen test. The authors then calculated that overall sensitivity of the lateral flow tests to be 95.4%, with a specificity of 89.1%. In addition, positive lateral flow and PCR tests were correlated (r = 0.968).
The authors discussed how these findings indicated that using antigen tests for patients with mild to moderate symptoms, allows for a reliable and accurate detection of COVID-19 and which is comparable to PCR. They concluded that implementation of lateral flow tests should be accompanied by standardised training for operators, quality assurance of testing and a coordinated approach to services.
Leber W et al. Comparing the diagnostic accuracy of point-of-care lateral flow antigen testing for SARS-CoV-2 with RT-PCR in primary care (REAP-2). EClinicalMedicine 2021