Mammography is the most preferred method for breast cancer screening. In this study, computer-aided diagnosis (CAD) systems were used to improve the image quality of mammography images and to detect suspicious areas. The main contribution of this study is to reveal the optimal combination of various pre-processing algorithms to enable better interpretation and classification of mammography images because pre-processing algorithms significantly affect the accuracy of segmentation and classification methods. In this study, the effect of combinations of different preprocessing methods in differentiating benign and malignant breast lesions was investigated. All image processing algorithms used for lesion detection were used in the mini-MIAS database. In the first step, label information and pectoral muscle resulting from the acquisition of mammography images were removed. In the second step, median filter (MF), contrast limited adaptive histogram equalization (CLAHE), and unsharp masking (USM) algorithms with different combinations of the resolution and visibility of images are increased. In the third step, suspicious regions are extracted from the mammograms using the k-means clustering technique. Then, features were extracted from the obtained ROIs. Finally, feature datasets were classified as normal/abnormal, and benign/malign (two class classification) using Machine Learning algorithms. Test performance measures of the classification methods were examined. In both classifications made in the study, lower classification performance values were obtained when the CLAHE algorithm was used alone as a pre-processing method compared to other pre-processing combinations. When the median filter and unsharp masking algorithms are added to the CLAHE algorithm, the performance of the classification methods has increased. In terms of classification success, Support Vector Machines, Random Forest, and Neural Networks showed the best performance. It was found by comparing the performances of the classification methods that different preprocessing algorithms were effective in detecting the presence of breast lesions and distinguishing benign and malignant.

Background/purpose: To assess EDI-OCT (enhanced depth imaging optical coherence tomography) of choroid for inflammatory signs in children with polyarteritis nodosa (PAN) and adenosine deaminase-2 deficiency (DADA-2). Methods: In this cross-sectional study conducted between June 2017 and September 2018, we evaluated children diagnosed with PAN (n = 11) and DADA-2 (n = 4) and an age- and sex-matched control group (n = 15). Demographic and laboratory data were retrospectively analyzed from patient charts. Disease activity was assessed using the pediatric vasculitis activity score (PVAS). Choroidal images were obtained with spectral domain-OCT to measure choroidal thickness (ChT) at 5 points (750 and 1500 μm from the foveal center in the temporal and nasal quadrants and beneath the fovea), and to calculate the total subfoveal choroidal area (TCA), luminal area (LA), stromal area (SA), and the choroidal vascularity index (CVI). Results: The median (min-max) age was 8 (4–16) years in PAN patients, 6 (5–16) years in DADA-2 patients and 8 (8–10) years in control group at the OCT visit (p = 0.214). The ChT at 3 points and the TCA, LA, and SA were higher in children with both PAN and DADA-2 patients compared to those of the control group (p < 0.0001, p = 0.049, p = 0.007, p = 0.007, p = 0.006, p = 0.033, respectively). The CVI was similar in both groups. No association was observed between the OCT findings, PVAS, and the erythrocyte sedimentation rate, and serum leukocyte and C-reactive protein levels. Conclusion: Similar CVI scores were obtained from PAN and DADA2 patients under treatment and from healthy controls. Increased subfoveal ChT without any other signs of ocular involvement may suggest choroidal thickening as a sign of mild subclinical inflammation.

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Objectives: The aim of this study is to introduce the features of diagnostic tests. In addition, it will be demonstrated which performance measures can be used for diagnostic tests with binary results, the properties of these measures and how to interpret them. Materials and Methods: The evaluation of the diagnostic test performance measures may differ depending on whether the test result is numerical or binary. When the diagnostic test result is continuous numerical data, ROC analysis is often utilized. The performance of a diagnostic test with binary results are usually evaluated using the measures of sensitivity and specificity. However, there are some important measures other than these two measures for binary test results. These measures are predictive values, overall accuracy, diagnostic odds ratio, Youden index, and likelihood ratios. Results: A hypothetical data has been produced based on the studies conducted on the performance of rapid tests (Specific IgM/IgG) according to the RT-PCR test for Covid 19 in the literature. An example of a diagnostic test (Specific IgM/IgG) with a binary result is given and all measurements and their confidence interval are obtained for this data. The performance of rapid test was examined and interpreted. Conclusion: It is important to design and evaluate the performance of diagnostic/screening tests for health care. In this review, some basic definitions, performance measures that can be used only in evaluating the diagnostic tests with binary results and their confidence intervals are mentioned. Having many different measures provides different interpretations in the evaluation of test performance. Accurately predicting the performance of a diagnostic test depends on many factors. These factors can be study design, criteria of participant selection, sample size calculation, test methods etc. There are guidelines that ensure that all information regarding the conditions under which the study was conducted is in report, in terms of such factors. Therefore, these guidelines are recommended for use of the checklist by many publishers.

Objective: This study aimed to develop the Turkish version of Identification of Functional Ankle Instability (TV_IdFAI) scale and evaluate its validity and reliability. Methods: A total of 100 participants (54 men and 46 women; 50 volleyball players and 50 sedentary individuals) between 18 and 38 years of age were included this study. The construct validity, reference validity, sensitivity, specificity, and test–retest reliability of TV_IdFAI were evaluated. For the test–retest reliability, the scale was applied to all participants again in 10–14 days. A correlation between the scale scores and test–retest results was examined with intraclass correlation coefficient. To evaluate the construct validity, a factor analysis method was used. For reference validity, a sports physician evaluated all participants and the clinical diagnoses were compared with total score of the scale. Sensitivity and specificity were calculated to evaluate the classification success of the scale with specified cutoff. Results: TV_IdFAI scale was grouped under two separate factors. It was determined that the variance for factor 1, factor 2 and for scale was 46.68%, 15.70%, and 62.38%, respectively. There was a statistically significant relationship 0.74 (95% CI 0.64–0.84; p<0.001) between the physician’s diagnosis and TV_IdFAI in terms of reference validity. The sensitivity and specificity of TV_IdFAI was 0.61 and 0.80, respectively. The reliability of TV_IdFAI was 0.94 (95% CI 0.92–0.96; p<0.001). Conclusion: This study shows that TV_IdFAI is a simple, easy to apply, reliable, and valid scale to define functional ankle instability in Turkish population. Level of Evidence: Level II, Diagnostic study Cite this article as: Tayfur A, Şendil A, Karakaya J, Ergun N. Cross-cultural adaptation, validity, and reliability of Turkish version of Identification of Functional Ankle Instability (IdFAI) scale. Acta Orthop Traumatol Turc 2020; 54(3): 300-4. DOI: 10.5152/j.aott.2020.03.256.