OBJECTIVES: The aim of this study was to evaluate applicability of the Qraycam device for detecting caries and filling body during tooth examinations. METHODS: Fifty-two subjects aged 25 to 34 years were recruited for tooth examination. Two examiners (an epidemiologic expert and a non-expert) performed visual tooth examination using only dental operating light, dental mirror, and air-drying without a dental explorer. Pictures or videos of every tooth surface were obtained under visual ray and 405 nm blue ray, respectively, by using Qraycam. The two examiners then evaluated these videos or images more than 7 days after visual examination. RESULTS: The results of the visual, visible ray image, and 405 nm blue ray image examinations showed very good kappa agreement with the gold standard for both examiners. The 405 nm blue ray image examination showed higher kappa agreement than visible ray image examination, and was similar to visual examination. Accuracy of detecting caries was enhanced by using 405 nm blue ray images from Qraycam. Accuracy of detecting filling body on 405 nm blue ray image examination was almost same as that by visual examination. CONCLUSIONS: Tooth examination with Qraycam images revealed high agreement with the gold standard and showed accuracy for detecting caries and filling body. Therefore, Qraycam would enhance the quality of oral epidemiologic survey including tooth examination and save cost and time of survey.
ROC Curve ; Tooth*

No abstract available.
ROC Curve ; Sensitivity and Specificity*

PURPOSE: To evaluate a scoring system based on ultrasonographic findings as a means of distinguishing between benign and malignant solid breast masses. MATERIALS AND METHODS: Histopathologically confirmed benign (n=102) and malignant (n=73) breast masses which ultrasonographic findings of histopathologic masses were reviewed for shape, border, internal echo, boundary echo, posterior echo, lateral echo, and ratio of transverse to anteroposterior diameter. There were statistically significant (p < 0.001 ; chi-square test) differences in each feature for benign and malignant lesions. The findings suggesting benignancy, equivocality, and malignancy of the masses were scored as 0, 1, and 2 respectively. The scores for all features were summed for each lesion. An ROC curve was obtained. RESULTS: When the score was 6, five benign and seven malignant cases were found. Scores of 0 and 1 indicated benign masses and a score above 11, malignancy. The turning point was a score of 6, so a score above 7 suggests malignancy. CONCLUSION: A ultrasonographic scoring system can be used to characterise breast masses. A score of a mass above 7 suggests probable malignancy and a score below 5 suggests benignancy.
Breast* ; ROC Curve ; Ultrasonography*

The purpose of this paper is to explain the making procedure and the usage of receiver operating characteristic curve(ROC) for interpretation of radiographic images. The conventional radiograms obtained after the creation of the lesions in the acrylic plates and were enhanced in color. The observer were informed of which tooth to examine, the 'a priori' probability of a lesion present and the approximate diameter of the lesions. The two groups of films were interpreted separately by the same observer using the same rating scale. The following rating scale was used: A; definitely no lesion, B; probably no lesion, C; not sure, D; probably a lesion, and E; definitely a lesion. In analysis, for each observer the diagnostic results in terms of true positive(TP) and false positive(FP) decisions were plotted on a graph. The lowest point on the graph represents the TP and FP when only decisions designated as E according to the rating scale are included. The next point shows the TP and FP values when diagnoses designated as D are added and so forth. By connecting such plot points, a receiver operating characteristic curves(ROC) is obtained. The area under the curve represents the diagnostic accuracy resulting from a diagnostic performance at pure chance level and a value of 1.0 at perfect performance. This method has been known as an useful method to detect the minute difference for each radiographic technic, each observer and for the different lesion depths.
Diagnosis ; ROC Curve ; Tooth

PURPOSE: To compare changes of anterior and posterior corneal elevation, and sagittal curvature according to the severity of keratoconus and to compare differences between keratoconus and normal cornea. METHODS: A total of 81 eyes diagnosed with keratoconus and 20 eyes of normal subjects were evaluated with a Pentacam Scheimpflug camera. The keratoconus eyes were divided into 3 groups according to mean keratometer (K): mild (K < or = 47.0 diopters (D)), moderate (47.0 to 52.0 D), and severe (52.0 D> or =). The following parameters were obtained to evaluate the correlation of keratoconus: corneal thickness, anterior and posterior corneal elevation, and sagittal curvature. RESULTS: Out of 81 keratoconus eyes, 56 eyes were mild, 12 eyes were moderate, and 13 eyes were severe keratoconus. The mean central corneal keratometer, anterior and posterior corneal elevation, and sagittal curvature of the keratoconus eyes were 49.7 D, 22.07 microm, 38.16 microm, 52.76 D and the values increased statistically compared to the normal eyes. Furthermore, the values increased significantly with the severity of keratoconus. ROC curve analysis showed the estimated meaningful value for anterior and posterior corneal elevation and sagittal curvature of keratoconus for diagnosis; there were no diagnostic values for corneal thickness and refractive power. CONCLUSIONS: The index of 5.5 microm for mean anterior elevation, 12.5 microm for mean posterior elevation, and 44.5 microm for mean sagittal curvature using the Pentacam(R) are useful to diagnose keratoconus. Variation of anterior and posterior elevation, and sagittal curvature measured by Pentacam(R) are useful in understanding the process of keratoconus.
Cornea ; Eye ; Keratoconus ; ROC Curve

The Receiver Operating Characteristic (ROC) curve has become a standard tool for the analysis and comparison of binary classifiers when the costs of misclassification are unknown. In fact ROC curve has replaced the correct rate or error rate. Extending this to the multiclass case has recently become a growing topic of interest. The conceptions and application of ROC curve are expounded. The history and some algorithms of the multi-class ROC surface are given in detail in this paper. Finally research trends of the multi-class ROC surface are approximating computing and visualization.
Algorithms ; Artificial Intelligence ; ROC Curve

PURPOSE: This study was designed to determine a proper threshold value and opacity in three-dimensional CT volume rendering of oral and maxillofacial area. MATERIALS AND METHODS: Three-dimensional CT data obtained from 50 persons who were done orthognatic surgery in department of oral and maxillofacial radiology of Seoul National University retrospectively. 12 volume rendering post-processing protocols of combination of threshold (100HU, 150HU, 221HU, 270HU) and opacity (58%, 80%, 90%) were applied. Five observers independently evaluated image quality using a five-point range scale. The results were analyzed by receiver operating characteristic curves, ANOVA and Kappa value. And three oromaxillofacial surgeons chose the all images that they thought proper clinically in the all of images. RESULTS: Analysis using ROC curves revealed the area under each curve which indicated a diagnostic accuracy. The highest diagnostic accuracy appear with 100HU and 58% opacity. and the lowest diagnostic accuracy appear with 221HU and 58% opacity that are being used protocol in department of oral and maxillofacial radiology of Seoul National University. But, no statistically significant difference was noted between any of the protocols. And the number of proper images clinically that chosen by three oromaxillofacial surgeons is the largest in the cases of protocol 8 (221HU, opacity 80%) and protocol 11 (270HU, opacity 80%) in one after the other. CONCLUSION: Threshold and opacity in volume rendering can be controled easily and these can be causes of making an diagnostic accuracy. So we need to select proper values of these factors.
Humans ; Retrospective Studies ; ROC Curve

OBJECTIVES: The purpose of this study was to evaluate the optimal distance and angle of observers by modulation transfer functions(MTFs) and receiver operating characteristics(ROCs). MATERIALS AND METHODS: Digital periapical radiograms were taken from 43 patients who have dental diseases(19 patients : dental caries, 12 patients : periapical lesions, 12 patients : periodontal diseases). Segmental images(4x4cm) were evaluated by 4 MTFs and ROC analysis. RESULTS: The optimal distance(magnification) using MTF by Mannos & Sakrison was 12.97, and those by Nill, Ngan and Rao were 8.39, 4.78, 5.84 respectively. The optimal distance obtained from 4 radiologists by ROC analysis was 32cm(Az value : 0.89), and it was 40cm(Az value : 0.78) from 4 non-radiologists. There were significant differences of Az values between 4 radiologists and 4 non-radiologists at 24, 32 and 40cm (p<0.05). No significant differences of optimal distances were observed using 4 MTFs among +20, +10, 0, -10, -20 degrees(p>0.05). The optimal angle obtained from 4 radiologists by ROC analysis was +20 degrees (Az value : 0.91), and no significant differences of Az values were observed among +20, +10, 0, -10 and 20 degrees(p>0.05). The optimal angle obtained from 4 non-radiologists by ROC analysis was 0 degree(Az value : 0.81), and no significant differences of Az values were observed among +20, +10, 0, -10 and 20 degrees(p>0.05). And there was significant difference of Az value between 4 radiologists and 4 non-radiologists at +20 degree(p<0.05), but no significant differences of Az values were observed among +10, 0, -10 and -20 degrees(p>0.05).
Dental Caries ; Humans ; ROC Curve

Receiver Operating Characteristic (ROC) Curves are frequently used in biomedical informatics research to evaluate classification and prediction models to support decision, diagnosis, and prognosis. ROC analysis investigates the accuracy of models and has ability to separate positive from negative cases. It is especially useful in evaluating predictive models and compare to other tests which produce output values in a continuous range. Empirical ROC curve is jagged but a true ROC curve is smooth. For this purpose kernel smoothing were used. The Area Under ROC Curve (AUC) frequently is used as a measure of the effectiveness of diagnostic markers. In this study we compare estimation of this area based on normal assumptions and kernel smoothing. This study used measurements of TSH from patients and non-diseased people of congenital hypothyroidism screening in Isfahan province. Using the method, TSH ROC curves from Isfahani's infants were fitted. For evaluating of accuracy of this test, AUC and its standard error calculated. Also effectiveness of the kernel methods in comparison to other methods showed.
ROC Curve ; Thyroid Gland ; Hypothyroidism

PURPOSE: To evaluate the diagnostic ability in differentiating between ameloblastoma and odontogenic keratocyst according to the imaging modalities and observers. MATERIALS AND METHODS: We evaluated thirty-six cases of ameloblastomas and forty-seven cases of odontogenic keratocysts all histologically confirmed. Six oral and maxillofacial radiologists diagnosed the lesions by 3 methods: using panoramic radiograph, using computed tomograph (CT), and using panoramic radiograph and CT. The observers were classified by 3 groups: group 1 had experienced over 10 years in oral and maxillofacial radiologic field, group 2 had experienced for 3-4 years, and group 3 was in the process of residentship. After over 2 weeks, the observers diagnosed them by the same methods. RESULTS: The ROC curve areas except for group 3 were the highest with interpretation using panoramic radiograph and CT, followed by interpretation using CT only, and the lowest with interpretation using panoramic radiograph only. The overall difference was not found in diagnostic ability among groups in using panoramic radiograph only, but there was difference in diagnostic ability of group 1 and 2 vs 3 in using CT only, and combination panoramic radiograph and CT. CONCLUSIONS: To differentiate between ameloblastoma and odontogenic keratocyst more accurately, the experienced oral and maxillofacial radiologist should diagnose with combination of panoramic radiograph and CT.
Ameloblastoma* ; Diagnosis, Differential* ; Odontogenic Cysts* ; ROC Curve