Clinical Outcomes and Complications of 2-Stage Septic Versus Aseptic Revision Total Knee Arthroplasty: A Systematic Review and Meta-Analysis.

BACKGROUND: Although several studies have compared the clinical outcomes of septic and aseptic revision total knee arthroplasty (TKA), their results have been controversial. Therefore, this study aimed to compare clinical outcomes and complications of septic and aseptic revision TKA through a systematic review and meta-analysis. METHODS: The PubMed (MEDLINE) and Embase databases were searched for studies evaluating the clinical outcomes and complications of 2-stage septic revision and aseptic revision TKAs. A systematic review of clinical outcomes (Knee Society Knee and Function Scores and range of motion) and complications (reoperation, infection, and failure rates) was conducted. RESULTS: Thirteen studies were included in the systematic review. The mean MINORS (Methodological Index for NOn-Randomized Studies) score of the included studies was 20.5 (range, 18 to 22). The meta-analysis revealed higher reoperation (risk ratio [RR], 1.98; 95% confidence interval [CI], 1.50 to 2.62; p < 0.00001), infection (RR, 4.08; 95% CI, 2.94 to 5.64; p < 0.00001), and failure rates (RR, 2.88; 95% CI, 1.38 to 6.03; p = 0.005) in septic revision TKAs than in aseptic revision TKAs. Moreover, septic revision TKAs showed lower Knee Society Knee Scores compared with aseptic TKAs (mean difference [MD], -6.86; 95% CI, -11.80 to -1.92; p = 0.006). However, the Knee Society Function Score (MD, -1.84; 95% CI, -7.84 to 3.80; p = 0.52) and range of motion (MD, -6.96°; 95% CI, -16.23° to 2.31°; p = 0.14) were not significantly different between septic and aseptic revision TKAs. CONCLUSIONS: Despite the heterogeneity of prosthesis designs and surgical protocols used in septic and aseptic revision TKAs, the results of this systematic review suggest that 2-stage septic revision TKAs have poorer clinical outcomes and higher complication rates than aseptic revision TKAs do. LEVEL OF EVIDENCE: Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

Misdiagnosis of the acute phase of undifferentiated arthritis as pyogenic arthritis: A case report and literature review.

Undifferentiated arthritis is a disease that clinically presents with symptoms and signs of inflammatory arthritis but does not meet the specific diagnostic criteria of rheumatoid arthritis (RA) or spondyloarthropathy. Here, we report our experience with a patient whose diagnosis of RA was delayed due to a lack of evidence for RA. The patient complained of knee joint swelling and pain, but the clinical features did not match those of typical pyogenic arthritis. Because infection could not be completely ruled out, the patient was treated for pyogenic arthritis using arthroscopic synovectomy and antibiotics. However, the pain was not relieved and the rheumatologist suggested a diagnosis of undifferentiated monoarthritis, which is an early stage of RA. The pain eventually spread to other joints, leading to the diagnosis of RA, approximately two months after the initial visit. Considering undifferentiated arthritis and making appropriate differential diagnoses is important to avoid unnecessary treatments such as surgery or prolonged antibiotic use. Clinical relevance: Awareness of the possibility of undifferentiated monoarthritis, an early stage of RA, may be helpful in treating patients with recurrent knee effusion.

Localization-adjusted diagnostic performance and assistance effect of a computer-aided detection system for pneumothorax and consolidation.

While many deep-learning-based computer-aided detection systems (CAD) have been developed and commercialized for abnormality detection in chest radiographs (CXR), their ability to localize a target abnormality is rarely reported. Localization accuracy is important in terms of model interpretability, which is crucial in clinical settings. Moreover, diagnostic performances are likely to vary depending on thresholds which define an accurate localization. In a multi-center, stand-alone clinical trial using temporal and external validation datasets of 1,050 CXRs, we evaluated localization accuracy, localization-adjusted discrimination, and calibration of a commercially available deep-learning-based CAD for detecting consolidation and pneumothorax. The CAD achieved image-level AUROC (95% CI) of 0.960 (0.945, 0.975), sensitivity of 0.933 (0.899, 0.959), specificity of 0.948 (0.930, 0.963), dice of 0.691 (0.664, 0.718), moderate calibration for consolidation, and image-level AUROC of 0.978 (0.965, 0.991), sensitivity of 0.956 (0.923, 0.978), specificity of 0.996 (0.989, 0.999), dice of 0.798 (0.770, 0.826), moderate calibration for pneumothorax. Diagnostic performances varied substantially when localization accuracy was accounted for but remained high at the minimum threshold of clinical relevance. In a separate trial for diagnostic impact using 461 CXRs, the causal effect of the CAD assistance on clinicians' diagnostic performances was estimated. After adjusting for age, sex, dataset, and abnormality type, the CAD improved clinicians' diagnostic performances on average (OR [95% CI] = 1.73 [1.30, 2.32]; p < 0.001), although the effects varied substantially by clinical backgrounds. The CAD was found to have high stand-alone diagnostic performances and may beneficially impact clinicians' diagnostic performances when used in clinical settings.

A Deep Learning Model with High Standalone Performance for Diagnosis of Unruptured Intracranial Aneurysm.

PURPOSE: This study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to that of human radiologists for approval from the Korean Ministry of Food and Drug Safety as an artificial intelligence-applied software. MATERIALS AND METHODS: In this single-center, retrospective, confirmatory clinical trial, the diagnostic performance of the model was evaluated in a predetermined test set. After sample size estimation, the test set consisted of 135 aneurysm-containing examinations with 168 intracranial aneurysms and 197 aneurysm-free examinations. The target sensitivity and specificity were set as 87% and 92%, respectively. The patient-wise sensitivity and specificity of the model were analyzed. Moreover, the lesion-wise sensitivity and false-positive detection rate per case were also investigated. RESULTS: The sensitivity and specificity of the model were 91.11% [95% confidence interval (CI): 84.99, 95.32] and 93.91% (95% CI: 89.60, 96.81), respectively, which met the target performance values. The lesion-wise sensitivity was 92.26%. The overall false-positive detection rate per case was 0.123. Of the 168 aneurysms, 13 aneurysms from 12 examinations were missed by the model. CONCLUSION: The present deep learning model for automated detection of unruptured intracranial aneurysms on TOF MRA achieved the target diagnostic performance comparable to that of human radiologists. With high standalone performance, this model may be useful for accurate and efficient diagnosis of intracranial aneurysm.

Risk Factors and a Scoring System to Predict ARDS in Patients with COVID-19 Pneumonia in Korea: A Multicenter Cohort Study.

Predictive studies of acute respiratory distress syndrome (ARDS) in patients with coronavirus disease 2019 (COVID-19) are limited. In this study, the predictors of ARDS were investigated and a score that can predict progression to ARDS in patients with COVID-19 pneumonia was developed. All patients who were diagnosed with COVID-19 pneumonia between February 1, 2020, and May 15, 2020, at five university hospitals in Korea were enrolled. Their demographic, clinical, and epidemiological characteristics and the outcomes were collected using the World Health Organization COVID-19 Case Report Form. A logistic regression analysis was performed to determine the predictors for ARDS. The receiver operating characteristic (ROC) curves were constructed for the scoring model. Of the 166 patients with COVID-19 pneumonia, 37 (22.3%) patients developed ARDS. The areas under the curves for the infiltration on a chest X-ray, C-reactive protein, neutrophil/lymphocyte ratio, and age, for prediction of ARDS were 0.91, 0.90, 0.87, and 0.80, respectively (all P < 0.001). The COVID-19 ARDS Prediction Score (CAPS) was constructed using age (≥60 years old), C-reactive protein (≥5 mg/dL), and the infiltration on a chest X-ray (≥22%), with each predictor allocated 1 point. The area under the curve of COVID-19 ARDS prediction score (CAPS) for prediction of ARDS was 0.90 (95% CI 0.86-0.95; P < 0.001). It provided 100% sensitivity and 75% specificity when the CAPS score cutoff value was 2 points. CAPS, which consists of age, C-reactive protein, and the area of infiltration on a chest X-ray, was predictive of the development of ARDS in patients with COVID-19 pneumonia.

Automatic evaluation of fetal head biometry from ultrasound images using machine learning.

OBJECTIVE: Ultrasound-based fetal biometric measurements, such as head circumference (HC) and biparietal diameter (BPD), are frequently used to evaluate gestational age and diagnose fetal central nervous system pathology. Because manual measurements are operator-dependent and time-consuming, much research is being actively conducted on automated methods. However, the existing automated methods are still not satisfactory in terms of accuracy and reliability, owing to difficulties dealing with various artefacts in ultrasound images. APPROACH: Using the proposed method, a labeled dataset containing 102 ultrasound images was used for training, and validation was performed with 70 ultrasound images. MAIN RESULTS: A success rate of 91.43% and 100% for HC and BPD estimations, respectively, and an accuracy of 87.14% for the plane acceptance check. SIGNIFICANCE: This paper focuses on fetal head biometry and proposes a deep-learning-based method for estimating HC and BPD with a high degree of accuracy and reliability.

Automatic 3D cephalometric annotation system using shadowed 2D image-based machine learning.

This paper presents a new approach to automatic three-dimensional (3D) cephalometric annotation for diagnosis, surgical planning, and treatment evaluation. There has long been considerable demand for automated cephalometric landmarking, since manual landmarking requires considerable time and experience as well as objectivity and scrupulous error avoidance. Due to the inherent limitation of two-dimensional (2D) cephalometry and the 3D nature of surgical simulation, there is a trend away from current 2D to 3D cephalometry. Deep learning approaches to cephalometric landmarking seem highly promising, but there exist serious difficulties in handling high dimensional 3D CT data, dimension referring to the number of voxels. To address this issue of dimensionality, this paper proposes a shadowed 2D image-based machine learning method which uses multiple shadowed 2D images with various lighting and view directions to capture 3D geometric cues. The proposed method using VGG-net was trained and tested using 2700 shadowed 2D images and corresponding manual landmarkings. Test data evaluation shows that our method achieved an average point-to-point error of 1.5 mm for the seven major landmarks.

CT sinogram-consistency learning for metal-induced beam hardening correction.

PURPOSE: This paper proposes a sinogram-consistency learning method to deal with beam hardening-related artifacts in polychromatic computerized tomography (CT). The presence of highly attenuating materials in the scan field causes an inconsistent sinogram that does not match the range space of the Radon transform. When the mismatched data are entered into the range space during CT reconstruction, streaking and shading artifacts are generated owing to the inherent nature of the inverse Radon transform METHODS: The proposed learning method aims to repair inconsistent sinogram by removing the primary metal-induced beam hardening factors along the metal trace in the sinogram. Taking account of the fundamental difficulty in obtaining sufficient training data in a medical environment, the learning method is designed to use simulated training data and a patient's implant type-specific learning model is used to simplify the learning process. RESULTS: The feasibility of the proposed method is investigated using a dataset, consisting of real CT scans of pelvises containing simulated hip prostheses. The anatomical areas in training and test data are different, in order to demonstrate that the proposed method extracts the beam hardening features, selectively. The results show that our method successfully corrects sinogram inconsistency by extracting beam hardening sources by means of deep learning. CONCLUSION: This paper proposed a deep learning method of sinogram correction for beam hardening reduction in CT for the first time. Conventional methods for beam hardening reduction are based on regularizations, and have the fundamental drawback of being not easily able to use manifold CT images, while a deep learning approach has the potential to do so.

Deep learning for undersampled MRI reconstruction.

This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting the image-folding problem dictated by the Poisson summation formula. To deal with the localization uncertainty due to image folding, a small number of low-frequency k-space data are added. Training the deep learning net involves input and output images that are pairs of the Fourier transforms of the subsampled and fully sampled k-space data. Our experiments show the remarkable performance of the proposed method; only 29[Formula: see text] of the k-space data can generate images of high quality as effectively as standard MRI reconstruction with the fully sampled data.

In situ visualization of gene expression using polymer-coated quantum-dot-DNA conjugates.

Imaging of specific mRNA targets in cells is of great importance in understanding gene expression and cell signaling processes. Subcellular localization of mRNA is known as a universal mechanism for cells to sequester specific mRNA for high production of required proteins. Various gene expressions in Drosophila cells are studied using quantum dots (QDs) and the fluorescence in situ hybridization (FISH) method. The excellent photostability and highly luminescent properties of QDs compared to conventional fluorophores allows reproducible obtainment of quantifiable mRNA gene expression imaging. Amine-modified oligonucleotide probes are designed and covalently attached to the carboxyl-terminated polymer-coated QDs via EDC chemistry. The resulting QD-DNA conjugates show sequence-specific hybridization with target mRNAs. Quantitative analysis of FISH on the Diptericin gene after lipopolysaccharide (LPS) treatment shows that the intensity and number of FISH signals per cell depends on the concentration of LPS and correlates well with quantitative real-time PCR results. In addition, our QD-DNA probes exhibit excellent sensitivity to detect the low-expressing Dorsal-related immunity factor gene. Importantly, multiplex FISH of Ribosomal protein 49 and Actin 5C using green and red QD-DNA conjugates allows the observation of cellular distribution of the two independent genes simultaneously. These results demonstrate that highly fluorescent and stable QD-DNA probes can be a powerful tool for direct localization and quantification of gene expression in situ.