Eye Melanoma Diagnosis System using Statistical Texture Feature Extraction and Soft Computing Techniques.

Background: Eye melanoma is deforming in the eye, growing and developing in tissues inside the middle layer of an eyeball, resulting in dark spots in the iris section of the eye, changes in size, the shape of the pupil, and vision. Objective: The current study aims to diagnose eye melanoma using a gray level co-occurrence matrix (GLCM) for texture extraction and soft computing techniques, leading to the disease diagnosis faster, time-saving, and prevention of misdiagnosis resulting from the physician's manual approach. Material and Methods: In this experimental study, two models are proposed for the diagnosis of eye melanoma, including backpropagation neural networks (BPNN) and radial basis functions network (RBFN). The images used for training and validating were obtained from the eye-cancer database. Results: Based on our experiments, our proposed models achieve 92.31% and 94.70% recognition rates for GLCM+BPNN and GLCM+RBFN, respectively. Conclusion: Based on the comparison of our models with the others, the models used in the current study outperform other proposed models.

Comparison of Diagnostic Test Accuracy of Cone-Beam Breast Computed Tomography and Digital Breast Tomosynthesis for Breast Cancer: A Systematic Review and Meta-Analysis Approach.

BACKGROUND: Cone-beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) remain the main 3D modalities for X-ray breast imaging. This study aimed to systematically evaluate and meta-analyze the comparison of diagnostic accuracy of CBBCT and DBT to characterize breast cancers. METHODS: Two independent reviewers identified screening on diagnostic studies from 1 January 2015 to 30 December 2021, with at least reported sensitivity and specificity for both CBBCT and DBT. A univariate pooled meta-analysis was performed using the random-effects model to estimate the sensitivity and specificity while other diagnostic parameters like the area under the ROC curve (AUC), positive likelihood ratio (LR+), and negative likelihood ratio (LR-) were estimated using the bivariate model. RESULTS: The pooled sensitivity specificity, LR+ and LR- and AUC at 95% confidence interval are 86.7% (80.3-91.2), 87.0% (79.9-91.8), 6.28 (4.40-8.96), 0.17 (0.12-0.25) and 0.925 for the 17 included studies in DBT arm, respectively, while, 83.7% (54.6-95.7), 71.3% (47.5-87.2), 2.71 (1.39-5.29), 0.20 (0.04-1.05), and 0.831 are the pooled sensitivity specificity, LR+ and LR- and AUC for the five studies in the CBBCT arm, respectively. CONCLUSIONS: Our study demonstrates that DBT shows improved diagnostic performance over CBBCT regarding all estimated diagnostic parameters; with the statistical improvement in the AUC of DBT over CBBCT. The CBBCT might be a useful modality for breast cancer detection, thus we recommend more prospective studies on CBBCT application.

Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis.

RATIONALE AND OBJECTIVE: To perform a meta-analysis to compare the diagnostic test accuracy (DTA) of deep learning (DL) in detecting coronavirus disease 2019 (COVID-19), and to investigate how network architecture and type of datasets affect DL performance. MATERIALS AND METHODS: We searched PubMed, Web of Science and Inspec from January 1, 2020, to December 3, 2020, for retrospective and prospective studies on deep learning detection with at least reported sensitivity and specificity. Pooled DTA was obtained using random-effect models. Sub-group analysis between studies was also carried out for data source and network architectures. RESULTS: The pooled sensitivity and specificity were 91% (95% confidence interval [CI]: 88%, 93%; I2 = 69%) and 92% (95% CI: 88%, 94%; I2 = 88%), respectively for 19 studies. The pooled AUC and diagnostic odds ratio (DOR) were 0.95 (95% CI: 0.88, 0.92) and 112.5 (95% CI: 57.7, 219.3; I2 = 90%) respectively. The overall accuracy, recall, F1-score, LR+ and LR- are 89.5%, 89.5%, 89.7%, 23.13 and 0.13. Sub-group analysis shows that the sensitivity and DOR significantly vary with the type of network architectures and sources of data with low heterogeneity are (I2 = 0%) and (I2 = 18%) for ResNet architecture and single-source datasets, respectively. CONCLUSION: The diagnosis of COVID-19 via deep learning has achieved incredible performance, and the source of datasets, as well as network architectures, strongly affect DL performance.

Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features.

To assess if quantitative integrated deep learning and radiomics features can predict the PD-L1 expression level in preoperative MRI of hepatocellular carcinoma (HCC) patients. The data in this study consist of 103 hepatocellular carcinoma patients who received immunotherapy in a single center. These patients were divided into a high PD-L1 expression group (30 patients) and a low PD-L1 expression group (73 patients). Both radiomics and deep learning features were extracted from their MRI sequence of T2-WI, which were merged into an integrative feature space for machine learning for the prediction of PD-L1 expression. The five-fold cross-validation was adopted to validate the performance of the model, while the AUC was used to assess the predictive ability of the model. Based on the five-fold cross-validation, the integrated model achieved the best prediction performance, with an AUC score of 0.897 ± 0.084, followed by the deep learning-based model with an AUC of 0.852 ± 0.043 then the radiomics-based model with AUC of 0.794 ± 0.035. The feature set integrating radiomics and deep learning features is more effective in predicting PD-L1 expression level than only one feature type. The integrated model can achieve fast and accurate prediction of PD-L1 expression status in preoperative MRI of HCC patients.

Prevalence of COVID-19 Diagnostic Output with Chest Computed Tomography: A Systematic Review and Meta-Analysis.

BACKGROUND: The pooled prevalence of chest computed tomography (CT) abnormalities and other detailed analysis related to patients' biodata like gender and different age groups have not been previously described for patients with coronavirus disease 2019 (COVID-19), thus necessitating this study. Objectives: To perform a meta-analysis to evaluate the diagnostic performance of chest CT, common CT morphological abnormalities, disease prevalence, biodata information, and gender prevalence of patients. METHODS: Studies were identified by searching PubMed and Science Direct libraries from 1 January 2020 to 30 April 2020. Pooled CT positive rate of COVID-19 and RT-PCR, CT-imaging features, history of exposure, and biodata information were estimated using the quality effect (QE) model. RESULTS: Out of 36 studies included, the sensitivity was 89% (95% CI: 80-96%) and 98% (95% CI: 90-100%) for chest CT and reverse transcription-polymerase chain reaction (RT-PCR), respectively. The pooled prevalence across lesion distribution were 72% (95% CI: 62-80%), 92% (95% CI: 84-97%) for lung lobe, 88% (95% CI: 81-93%) for patients with history of exposure, and 91% (95% CI: 85-96%) for patients with all categories of symptoms. Seventy-six percent (95% CI: 67-83%) had age distribution across four age groups, while the pooled prevalence was higher in the male with 54% (95% CI: 50-57%) and 46% (95% CI: 43-50%) in the female. CONCLUSIONS: The sensitivity of RT-PCR was higher than chest CT, and disease prevalence appears relatively higher in the elderly and males than children and females, respectively.