Project description:ObjectiveTo evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images.Materials and methodsThis study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists.ResultsA total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 × 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds.ConclusionOur CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

Project description:BackgroundThe Rib Index, (RI), extracted from the double rib contour sign (DRCS) on lateral spinal radiographs to evaluate rib hump deformity, (RHD), in idiopathic scoliosis, (IS), patients, has been previously introduced. Although various papers using the RI have been published, no study on its reproducibility has been reported. The aim of this report is to estimate the variations of the RI in a number of a pair set of lateral chest radiographs (LCRs). The hypothesis was that the RI should have minimal variability for each subject having successive LCRs.MethodsSeventy randomized patients who were treated in the hospital for lung diseases (mainly pneumonia or other communicable lung diseases), were initially included in the study. Each of these patients had two successive LCRs (named A and B group of radiographs) at the radiological department of the hospital, by the same technician, during the course of their treatment. The radiation source - patient distance was constant. LCRs obtained at an incorrect patient's position, or from patients who underwent a thoracic intervention and all LCRs with symmetric hemi-thoraces were excluded from the study. The LCRs of 49 patients were deemed suitable for inclusion in the study. The RI was calculated in both (A and B) LCRs of each patient. The statistical analysis included the following techniques: paired t-test, Pearson correlation coefficient and intra- and inter-observer error using the formula (SD/√2)/2, where SD is this of the differences of the two sets of measurement (As-Bs). The SPSS v16 statistical package was used.ResultsIn the 49 pairs of LCRs there was no statistical difference of the RI, (paired t-test p< 0.314). The RI in the A and B group of LCRs was perfectly correlated (correlation coefficient = 0,924, p < 0.0001). The intra-observer error was 0.0080 while the inter-observer error 0.0213 in terms of 95% CI.ConclusionThe RI proves to be a reliable method to evaluate the thoracic deformity and the effect of surgical or non-operative treatment on the IS RHD. RI is a simple method, a safe reproducible way to assess the RHD based on lateral radiographs, without the need for any further special radiographs and exposure to additional radiation.

Project description:BackgroundOperative indications for supination-external rotation (SER) ankle fractures depend on the integrity of the medial structures. Despite the importance of assessing deep deltoid ligament injuries, the accuracy of common diagnostic tests has not been established.Questions/purposesThe objective of this study was to compare the ability of injury (non-stress) and stress radiographs and magnetic resonance imaging (MRI) to diagnose deep deltoid ligament ruptures in operative SER ankle fractures.MethodsPatients were included who underwent surgical fixation of SER ankle fractures and had appropriate injury and manual stress test radiographs, pre-operative ankle MRI, and intra-operative assessment of deep deltoid integrity by direct visualization. The medial clear space (MCS) was considered positive for all values over 5 mm on the injury or stress mortise radiographs. MRI analysis of the deep deltoid ligament injury was performed by blinded fellowship-trained musculoskeletal radiologists. Intra-operative direct visualization and assessment of the deltoid was performed using a direct medial ankle approach at the time of operative fracture fixation.ResultsUsing intra-operative visualization as the gold standard, MCS measurements and MRI had differing abilities to diagnose a deep deltoid rupture. In cases where the MCS was less than 5 mm on injury radiographs and stress tests were performed, MCS measurements were much less accurate than MRI in predicting deltoid ruptures (46% versus 79%, respectively) with a high false positive rate (80%). In contrast, an MCS measurement of greater than 5 mm on injury radiographs was a strong predictor of deep deltoid rupture (accuracy of 95%).ConclusionCompared with direct visualization of the deltoid ligament intra-operatively, these data support proceeding with surgery when the MCS on injury radiographs is greater than 5 mm without any additional stress tests or advanced imaging. When the MCS is less than 5 mm, we recommend MRI analysis because of its increased accuracy and decreased false positive rate. Improving our ability to diagnose deltoid ruptures will contribute to more effective management of patients with SER ankle fractures.

Project description:BackgroundChest radiography to diagnose and screen for pulmonary tuberculosis has limitations, especially due to inter-reader variability. Automating the interpretation has the potential to overcome this drawback and to deliver objective and reproducible results. The CAD4TB software is a computer-aided detection system that has shown promising preliminary findings. Evaluation studies in different settings are needed to assess diagnostic accuracy and practicability of use.MethodsCAD4TB was evaluated on chest radiographs of patients with symptoms suggestive of pulmonary tuberculosis enrolled in two cohort studies in Tanzania. All patients were characterized by sputum smear microscopy and culture including subsequent antigen or molecular confirmation of Mycobacterium tuberculosis (M.tb) to determine the reference standard. Chest radiographs were read by the software and two human readers, one expert reader and one clinical officer. The sensitivity and specificity of CAD4TB was depicted using receiver operating characteristic (ROC) curves, the area under the curve calculated and the performance of the software compared to the results of human readers.ResultsOf 861 study participants, 194 (23%) were culture-positive for M.tb. The area under the ROC curve of CAD4TB for the detection of culture-positive pulmonary tuberculosis was 0.84 (95% CI 0.80-0.88). CAD4TB was significantly more accurate for the discrimination of smear-positive cases against non TB patients than for smear-negative cases (p-value<0.01). It differentiated better between TB cases and non TB patients among HIV-negative compared to HIV-positive individuals (p<0.01). CAD4TB significantly outperformed the clinical officer, but did not reach the accuracy of the expert reader (p?=?0.02), for a tuberculosis specific reading threshold.ConclusionCAD4TB accurately distinguished between the chest radiographs of culture-positive TB cases and controls. Further studies on cost-effectiveness, operational and ethical aspects should determine its place in diagnostic and screening algorithms.

Project description:ObjectivesTo assess inter-reader agreements and diagnostic accuracy of chest CT to identify COVID-19 pneumonia in patients with intermediate clinical probability during an acute disease outbreak.MethodsFrom March 20 to April 8, 319 patients (mean age 62.3 years old) consecutive patients with an intermediate clinical probability of COVID-19 pneumonia underwent a chest CT scan. Two independent chest radiologists blinded to clinical information and RT-PCR results retrospectively reviewed and classified images on a 1-5 confidence level scale for COVID-19 pneumonia. Agreements between radiologists were assessed with kappa statistics. Diagnostic accuracy of chest CT compared with RT-PCR assay and patient outcomes was measured using receiver operating characteristics (ROC). Positive predictive value (PPV) and negative predictive value (NPV) for COVID-19 pneumonia were calculated.ResultsInter-observer agreement for highly probable (kappa: 0.83 [p < .001]) and highly probable or probable (kappa: 0.82 [p < .001]) diagnosis of COVID-19 pneumonia was very good. RT-PCR tests performed in 307 patients were positive in 174 and negative in 133. The areas under the curve (AUC) were 0.94 and 0.92 respectively. With a disease prevalence of 61.2%, PPV were 95.9% and 94.3%, and NPV 84.4% and 77.1%.ConclusionDuring acute COVID-19 outbreak, chest CT scan may be used for triage of patients with intermediate clinical probability with very good inter-observer agreements and diagnostic accuracy.Key points• Concordances between two chest radiologists to diagnose or exclude a COVID-19 pneumonia in 319 consecutive patients with intermediate clinical probability were very good (kappa: 0.82; p < .001). • When compared with RT-PCR results and patient outcomes, the diagnostic accuracy of CT to identify COVID-19 pneumonia was high for both radiologists (AUC: 0.94 and 0.92). • With a disease prevalence of 61.2% in the studied population, the positive predictive values of CT for diagnosing COVID-19 pneumonia were 95.9% and 94.3% with negative predictive values of 84.4% and 77.1%.

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Chest radiographs versus CT for the detection of rib fractures in children (DRIFT): a diagnostic accuracy observational study.

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