• Imaging Science in Dentistry
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• 제33권2호
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• pp.91-95
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• 2003

Purpose : To compare the diagnostic accuracy of proximal caries detection between Kodak Insight film and the Biomedisys CDX2000HQ digital (CCD) sensor. Materials and Methods: 156 proximal surfaces of extracted teeth, 78 of which had chemical artificial caries, were used in this study. Four observers interpreted the radiographs using a five-point confidence rating scale to record their diagnoses. The results were analyzed by receiver operating characteristic curves, ANOVA and Kappa values. Result: Analysis using receiver operating characteristic curves revealed the areas under each curve which indicated a diagnostic accuracy of 0.951 in Insight and 0.952 in CDX2000HQ digital sensor. ANOVA revealed no significant differences between the two images with respect to caries detection. Kappa values indicated that the mean intra-observer agreement was 0.85 and inter-observer agreement 0.71 in conventional radiography. In digital radiography, the mean intra-observer agreement was 0.84 and inter-observer agreement 0.72. Conclusion: The results suggest that no significant difference exists between the two modalities for artificial caries detection and that CDX2000HQ was as good as Insight film for this purpose.

• Asian Pacific Journal of Cancer Prevention
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• 제13권11호
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• pp.5517-5523
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• 2012

Purpose: To evaluate the diagnostic accuracy of ultrasonograph and fine-needle aspiration cytologic examination (USG-FNAC) in the staging of axillary lymph node metastasis in breast cancer patients.Methods: We conducted an electronic search of the literature addressing the performance of USG-FNAC in diagnosis of axillary lymph node metastasis in databases such as Pubmed, Medline, Embase, Ovid and Cochrane library. We introduced a series of diagnostic test indices to evaluate the performance of USG-FNAC by the random effect model (REM), including sensitivity, specificity, likelihood ratios, and diagnostic odds ratios and area under the curve (AUC). Results: A total of 20 studies including 1371 cases and 1289 controls were identified. The pooled sensitivity was determined to be 0.66 (95% CI 0.64-0.69), specificity 0.98 (95% CI 0.98-0.99), positive likelihood ratio 22.7 (95% CI 15.0-34.49), negative likelihood ratio 0.32 (95% CI 0.25-0.41), diagnostic OR 84.2 (95% CI 53.3-133.0). Due to the marginal threshold effect found in some indices of diagnostic validity, we used a summary SROC curve to aggregate data, and obtained a symmetrical curve with an AUC of 0.942. Conclusion: The results of this meta-analysis indicated that the USG-FNAC techniques have acceptable diagnostic validity indices and can be used for early staging of axillary lymph node in breast cancer patients.

• Asian Pacific Journal of Cancer Prevention
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• 제15권11호
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• pp.4705-4709
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• 2014

Objective: To determine the accuracy of magnetic resonance imaging (MRI) in detection of metastasis in pelvic and para-aortic lymph nodes from different gynecological malignancies. Materials and Methods: This retrospective cross sectional analytic study was conducted at the Department of Diagnostic Radiology, Aga Khan University Hospital Karachi Pakistan from January 2011 to December 2012. A sample of 48 women, age range between 20-79 years, fulfilling inclusion criteria were included. All patients had histopathologically proven gynecological malignancies in the cervix, endometrium or ovary and presented for a pretreatment MRI to our radiology department. Results: MRI was 100% sensitive and had a 100% positive predictive value to detect lymph node metastasis in lymph nodes with spiculated margins and 100% sensitive with a 75% positive predictive value to detect lymph node metastasis in a lymph node with lobulated margins. The sensitivity and positive predictive value of MRI to detect heterogeneous nodal enhancement were 100% and 75% respectively. Conclusions: Our study results reinforce that MRI should be used as a modality of choice in the pretreatment assessment of lymph nodes in proven gynaecological malignancies in order to determine the line of patientmanagement, distinguishing surgical from non-surgical cases.

• 대한수의학회지
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• 제44권4호
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• pp.669-676
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• 2004

To evaluate the diagnostic accuracy of two commercial ELISA tests (Allied- and CSL-ELISA) for the diagnosis of Mycobacterium paratuberculosis in cattle, Meta-analysis using English language papers published during 1990-2001 was performed. Diagnostic odds ratios (DOR) were analyzed using regression analysis together with summary receiver operating characteristic (ROC) curves. The difference in diagnostic performance between the two ELISA systems was evaluated by using linear regression. Publication bias was assessed by funnel plot and linear regression. The pooled sensitivity and specificity were 44% (95% CI, 38 to 51) and 98% (95% CI, 96 to 99) for the random-effect model. The DOR between studies was heterogeneous. The area under the fitted ROC curve (AUC) was 0.72 for the unweighted and 0.77 for the weighted model. Maximum joint sensitivity and specificity for the unweighted and weighted model from their summary ROC curve were 70% and 75%, respectively. Based on the fitted model, at a specificity of 95%, sensitivity was estimated to be 52% for the unweighted and 57% for the weighted model. From the final multivariable model study characteristic, the country was the only significant variable with an explained component variance of 13.3%. There were no significant differences in discriminatory power, sensitivity, and specificity between the two ELISA tests. The overall diagnostic accuracy of two commercial ELISA tests was moderate, as judged by the AUC, maximum joint sensitivity and specificity, and estimates from the fitted model and clinical usefulness of the tests for screening program is limited because of low sensitivity and heterogeneous of DOR. It is, therefore, recommended to use ELISA tests as a parallel testing with other diagnostic tests together to increase test sensitivity in the screening program.

• 대한세포병리학회지
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• 제18권2호
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• pp.93-99
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• 2007

Fine needle aspiration has been widely used to diagnose of breast lesions whether they are malignant or not. When applied by experienced and well-trained practitioners, its accuracy can approach that of histopathology. In order to make optimal use of FNAB in breast lesions, this article has reviewed the criteria for sample adequacy, the diagnostic terminology and the cytomorphologic approach to making a diagnosis and avoiding diagnostic pitfalls.

• 대한세포병리학회지
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• 제19권2호
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• pp.86-98
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• 2008

Cerebrospinal fluid (CSF) cytology is based on the cytopathologic findings of other body fluids. However, CSF's cytologic features are less familiar to physicians than are those of the other body fluid's cytology because of the small number of cases. The low overall diagnostic accuracy and the presence of false positivity still remain as problems. The incidence of lymphoreticular malignancies and metastatic carcinomas are rather higher than that of primary brain tumors. In this review, the characteristic cytologic findings of conventional CSF cytology are reviewed along with a brief note on the technical preparation and diagnostic pitfalls.

• Biomolecules & Therapeutics
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• 제30권2호
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• pp.179-183
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• 2022

Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid development of computer vision, automated deep learning is now used to classify microscopic images, including medical images. Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels. The images were divided into training (53.8%) and test (46.2%) sets (21,017 and 18,016 images, respectively). When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy ("no tumor") was 99.87%. Thus, the deep learning model distinguished metastatic lesions from normal lung tissue. Our approach will allow the rapid and accurate analysis of various tissues.

• Imaging Science in Dentistry
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• 제46권2호
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• pp.93-101
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• 2016

Purpose: The objective of this study was to investigate the effect of varying resolutions of cone-beam computed tomography images on the accuracy of linear measurements of edentulous areas in human cadaver heads. Intact cadaver heads were used to simulate a clinical situation. Materials and Methods: Fiduciary markers were placed in the edentulous areas of 4 intact embalmed cadaver heads. The heads were scanned with two different CBCT units using a large field of view ($13cm{\times}16cm$) and small field of view ($5cm{\times}8cm$) at varying voxel sizes (0.3 mm, 0.2 mm, and 0.16 mm). The ground truth was established with digital caliper measurements. The imaging measurements were then compared with caliper measurements to determine accuracy. Results: The Wilcoxon signed rank test revealed no statistically significant difference between the medians of the physical measurements obtained with calipers and the medians of the CBCT measurements. A comparison of accuracy among the different imaging protocols revealed no significant differences as determined by the Friedman test. The intraclass correlation coefficient was 0.961, indicating excellent reproducibility. Inter-observer variability was determined graphically with a Bland-Altman plot and by calculating the intraclass correlation coefficient. The Bland-Altman plot indicated very good reproducibility for smaller measurements but larger discrepancies with larger measurements. Conclusion: The CBCT-based linear measurements in the edentulous sites using different voxel sizes and FOVs are accurate compared with the direct caliper measurements of these sites. Higher resolution CBCT images with smaller voxel size did not result in greater accuracy of the linear measurements.

• Korean Journal of Radiology
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• 제22권3호
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• pp.442-453
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• 2021

Artificial intelligence (AI) will likely affect various fields of medicine. This article aims to explain the fundamental principles of clinical validation, device approval, and insurance coverage decisions of AI algorithms for medical diagnosis and prediction. Discrimination accuracy of AI algorithms is often evaluated with the Dice similarity coefficient, sensitivity, specificity, and traditional or free-response receiver operating characteristic curves. Calibration accuracy should also be assessed, especially for algorithms that provide probabilities to users. As current AI algorithms have limited generalizability to real-world practice, clinical validation of AI should put it to proper external testing and assisting roles. External testing could adopt diagnostic case-control or diagnostic cohort designs. A diagnostic case-control study evaluates the technical validity/accuracy of AI while the latter tests the clinical validity/accuracy of AI in samples representing target patients in real-world clinical scenarios. Ultimate clinical validation of AI requires evaluations of its impact on patient outcomes, referred to as clinical utility, and for which randomized clinical trials are ideal. Device approval of AI is typically granted with proof of technical validity/accuracy and thus does not intend to directly indicate if AI is beneficial for patient care or if it improves patient outcomes. Neither can it categorically address the issue of limited generalizability of AI. After achieving device approval, it is up to medical professionals to determine if the approved AI algorithms are beneficial for real-world patient care. Insurance coverage decisions generally require a demonstration of clinical utility that the use of AI has improved patient outcomes.

• 대한핵의학회지
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• 제38권3호
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• pp.209-217
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• 2004

Evaluation of dementia in patients with early symptoms of cognitive decline is clinically challenging, but the need for early, accurate diagnosis has become more crucial, since several medication for the treatment of mild to moderate Alzheimer' disease are available. Many neurodegenerative diseases produce significant brain function alteration even when structural imaging (CT or MRI) reveal no specific abnormalities. The role of PET and SPECT brain imaging in the initial assessment and differential diagnosis of dementia is beginning to evolve vapidly and growing evidence indicates that appropriate incorporation of PET into the clinical work up can improve diagnostic and prognostic accuracy with respect to Alzheimer's disease, the most common cause of dementia in the geriatric population. in the fast few years, studios comparing neuropathologic examination with PET have established reliable and consistent accuracy for diagnostic evaluations using PET - accuracies substantially exceeding those of comparable studies of diagnostic value of SPECT or of both modalities assessed side by side, or of clinical evaluations done without nuclear imaging. This review deals the role of functional brain imaging techniques in the evaluation of dementias and the role of nuclear neuroimaging in the early detection and diagnosis of Alzheimer's disease.