METHODS: Medline, Embase, Google Scholar, and Cochrane Library were searched from their inception until August 2023 to identify studies using VCAT to diagnose MCI/mild dementia. The primary outcome was to assess the diagnostic accuracy of the VCAT for detecting MCI/mild dementia through area under the receiver operating characteristic curve (AU-ROC) analysis. The secondary outcome was to explore the correlation between VCAT scores and MCI/mild dementia presence by comparing scores among patients with and without MCI/mild dementia. Pooled sensitivity, specificity, and area under the curve (AUC) were calculated.
RESULTS: Five studies with 1,446 older adults (mean age 64-68.3 years) were included. The percentage of participants with MCI/mild dementia versus controls ranged from 16.5% to 87% across studies. All studies were conducted in Asian populations, mostly Chinese, in Singapore and Malaysia. The pooled sensitivity was 80% [95% confidence interval (CI) 68%-88%] and the specificity was 75% (95% CI 68%-80%). The AU-ROCC was 0.77 (95% CI 0.73-0.81). Patients with MCI/mild dementia had lower VCAT scores than the controls (mean difference -6.85 points, p
METHODS: Baseline characteristics and laboratory results were collected and analyzed. Receiver operating characteristic (ROC) curve analysis was used to joint detection of inflammatory markers for influenza positive children, and the scatter-dot plots were used to compare the differences between severe and non-severe group.
RESULTS: Influenza B positive children had more bronchitis and pneumonia (P
METHODS: This single-centred prospective cohort study was conducted from January-to-June 2021, involving all patients admitted on suspicion of appendicitis. All patients were scored according to the Alvarado score, Appendicitis Inflammatory Response (AIR) score, Raja Isteri Pengiran Anak Saleha (RIPASA) score and Adult Appendicitis score (AAS). The final diagnosis for each patient was recorded. Sensitivity and specificity were calculated for each system. Receiver operating characteristic (ROC) curve was constructed for each scoring system, and the area under the curve (AUC) was calculated. Optimal cut-off scores were calculated using Youden's Index.
RESULTS: A total of 245 patients were recruited with 198 (80.8%) patients underwent surgery. RIPASA score had higher sensitivity and specificity than other scoring systems without being statistically significant (sensitivity 72.7%, specificity 62.3%, optimal score 8.5, AUC 0.724), followed by the AAS (sensitivity 60.2%, specificity 75.4%, optimal score 14, AUC 0.719), AIR score (sensitivity 76.7%, specificity 52.2%, optimal score 5, AUC 0.688) and Alvarado score (sensitivity 69.9%, specificity 62.3%, optimal score 5, AUC 0.681). Multiple logistic regression revealed anorexia (p-value 0.018), right iliac fossa tenderness (p-value 0.005) and guarding (p-value 0.047) as significant clinical factors independently associated with appendicitis.
CONCLUSION: Appendicitis scoring systems have shown moderate sensitivity and specificity in our population. The RIPASA scoring system has shown to be the most sensitive, specific and easy-to-use scoring system in the Malaysian population whereas the AAS is most accurate in excluding low-risk patients.
METHODOLOGY: Jaw sections containing 67 teeth (86 roots) were collected from nine fresh, unclaimed bodies that were due for cremation. Imaging was carried out to detect AP lesions using film and digital PR with a centred view (FP and DP groups); film and digital PR combining central with 10˚ mesially and distally angled (parallax) views (FPS and DPS groups). All specimens underwent histopathological examination to confirm the diagnosis of AP. Sensitivity, specificity and predictive values of PR were analysed using rater mean (n = 5). Receiver operating characteristics (ROC) analysis was carried out.
RESULTS: Sensitivity was 0.16, 0.37, 0.27 and 0.38 for FP, FPS, DP and DPS, respectively. Both FP and FPS had specificity and positive predictive values of 1.0, whilst DP and DPS had specificity and positive predictive values of 0.99. Negative predictive value was 0.36, 0.43, 0.39 and 0.44 for FP, FPS, DP and DPS, respectively. Area under the curve (AUC) for the various imaging methods was 0.562 (FP), 0.629 (DP), 0.685 (FPS), 0.6880 (DPS).
CONCLUSIONS: The diagnostic accuracy of single digital periapical radiography was significantly better than single film periapical radiography. The inclusion of two additional horizontal (parallax) angulated periapical radiograph images (mesial and distal horizontal angulations) significantly improved detection of apical periodontitis.
METHODS: Consecutive NAFLD patients who underwent liver biopsy were enrolled in this study and had two sets each of pSWE and TE examinations by a nurse and a doctor on the same day of liver biopsy procedure. The medians of the four sets of pSWE and TE were used for evaluation of diagnostic accuracy using area under receiver operating characteristic curve (AUROC). Intra-observer and inter-observer variability was analyzed using intraclass correlation coefficients.
RESULTS: Data for 100 NAFLD patients (mean age 57.1 ± 10.2 years; male 46.0%) were analyzed. The AUROC of TE for diagnosis of fibrosis stage ≥ F1, ≥ F2, ≥ F3, and F4 was 0.89, 0.83, 0.83, and 0.89, respectively. The corresponding AUROC of pSWE was 0.80, 0.72, 0.69, and 0.79, respectively. TE was significantly better than pSWE for the diagnosis of fibrosis stages ≥ F2 and ≥ F3. The intra-observer and inter-observer variability of TE and pSWE measurements by the nurse and doctor was excellent with intraclass correlation coefficient > 0.96.
CONCLUSION: Transient elastography was significantly better than pSWE for the diagnosis of fibrosis stage ≥ F2 and ≥ F3. Both TE and pSWE had excellent intra-observer and inter-observer variability when performed by healthcare personnel of different backgrounds.
METHODOLOGY: Forty participants with no evidence of LLD were recruited. Height and TL were measured. Reflective markers were attached at specific points in lower extremity and subjects walked in gait lab at a self-selected normal walking pace with artificial LLDs of 0, 1, 2, 3, and 4 cm simulated using shoe raise. Accommodation period of 30 min was given. Infrared cameras were used to capture the motion. Primary kinematic (knee flexion and pelvic obliquity (PO)) and secondary kinetic (ground reaction force (GRF)) were measured at right heel strike and left heel strike. Functional adaptation was analyzed and the postulated predictor indices (PIs) were used as a screening tool using height, LLD, and TL to notify significance.
RESULTS: There was a significant knee flexion component seen in height category of less than 170 cm. There was significant difference between LLD 3 cm and 4 cm. No significant changes were seen in PO and GRF. PIs of LLD/height and LLD/TL were analyzed using receiver operating characteristic curve. LLD/height as a PI with value of 1.75 was determined with specificity of 80% and sensitivity of 76%.
CONCLUSION: A height of less than 170 cm has significant changes in relation to LLD. PI using LLD/height appears to be a promising tool to identify patients at risk.
METHODS: In this study, we propose a multi-view secondary input residual (MV-SIR) convolutional neural network model for 3D lung nodule segmentation using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest computed tomography (CT) images. Lung nodule cubes are prepared from the sample CT images. Further, from the axial, coronal, and sagittal perspectives, multi-view patches are generated with randomly selected voxels in the lung nodule cubes as centers. Our model consists of six submodels, which enable learning of 3D lung nodules sliced into three views of features; each submodel extracts voxel heterogeneity and shape heterogeneity features. We convert the segmentation of 3D lung nodules into voxel classification by inputting the multi-view patches into the model and determine whether the voxel points belong to the nodule. The structure of the secondary input residual submodel comprises a residual block followed by a secondary input module. We integrate the six submodels to classify whether voxel points belong to nodules, and then reconstruct the segmentation image.
RESULTS: The results of tests conducted using our model and comparison with other existing CNN models indicate that the MV-SIR model achieves excellent results in the 3D segmentation of pulmonary nodules, with a Dice coefficient of 0.926 and an average surface distance of 0.072.
CONCLUSION: our MV-SIR model can accurately perform 3D segmentation of lung nodules with the same segmentation accuracy as the U-net model.
METHODS: We performed a prospective study of consecutive adults with NAFLD who were scheduled for a liver biopsy at a tertiary hospital in Malaysia. Patients underwent VLFF and CAP measurements on the same day as their liver biopsy. Histopathology analyses of liver biopsy specimens were reported according to the Nonalcoholic Steatohepatitis Clinical Research Network scoring system. Stereologic analysis was performed using grid-point counting method combined with the Delesse principle.
RESULTS: We analyzed data from 97 patients (mean age 57.0 ± 10.1 years; 44.33% male; 91.8% obese; 95.9% centrally obese). Based on histopathology analysis, the area under receiver operating characteristic curve (AUROC) for VLFF in detection of steatosis grade ≥S2 was 0.92 and for CAP the AUROC was 0.65 (P < .001). Based on stereological analysis, the AUROC for VLFF for detection of steatosis grade ≥S2 was 0.92 and for CAP the AUROC was 0.63, (P = .002); for identification of steatosis grade S3, the AUROC for VLFF was 0.92 and for CAP the AUROC was 0.68 (P < .001).
CONCLUSIONS: In a prospective study of patients with NAFLD undergoing liver biopsy analysis, we found VLFF to more accurately determine grade of hepatic steatosis than CAP.
METHODS: A review of the literature identified studies containing histology verified CAP data (M probe, vibration controlled transient elastography with FibroScan®) for grading of steatosis (S0-S3). Receiver operating characteristic analysis after correcting for center effects was used as well as mixed models to test the impact of covariates on CAP. The primary outcome was establishing CAP cut-offs for distinguishing steatosis grades.
RESULTS: Data from 19/21 eligible papers were provided, comprising 3830/3968 (97%) of patients. Considering data overlap and exclusion criteria, 2735 patients were included in the final analysis (37% hepatitis B, 36% hepatitis C, 20% NAFLD/NASH, 7% other). Steatosis distribution was 51%/27%/16%/6% for S0/S1/S2/S3. CAP values in dB/m (95% CI) were influenced by several covariates with an estimated shift of 10 (4.5-17) for NAFLD/NASH patients, 10 (3.5-16) for diabetics and 4.4 (3.8-5.0) per BMI unit. Areas under the curves were 0.823 (0.809-0.837) and 0.865 (0.850-0.880) respectively. Optimal cut-offs were 248 (237-261) and 268 (257-284) for those above S0 and S1 respectively.
CONCLUSIONS: CAP provides a standardized non-invasive measure of hepatic steatosis. Prevalence, etiology, diabetes, and BMI deserve consideration when interpreting CAP. Longitudinal data are needed to demonstrate how CAP relates to clinical outcomes.
LAY SUMMARY: There is an increase in fatty liver for patients with chronic liver disease, linked to the epidemic of the obesity. Invasive liver biopsies are considered the best means of diagnosing fatty liver. The ultrasound based controlled attenuation parameter (CAP) can be used instead, but factors such as the underlying disease, BMI and diabetes must be taken into account. Registration: Prospero CRD42015027238.