This retrospective clinical study was performed to evaluate whether a visual or quantitative method is more valuable for assessing painful temporomandibular disorder (TMD) using bone scintigraphy results.In total, 230 patients (172 women and 58 men) with TMD were enrolled. All patients were questioned about their temporomandibular joint (TMJ) pain. Bone scintigraphic data were acquired in all patients, and images were analyzed by visual and quantitative methods using the TMJ-to-skull uptake ratio. The diagnostic performances of both bone scintigraphic assessment methods for painful TMD were compared.In total, 241 of 460 TMJs (52.4%) were finally diagnosed with painful TMD. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the visual analysis for diagnosing painful TMD were 62.8%, 59.6%, 58.6%, 63.8%, and 61.1%, respectively. The quantitative assessment showed the ability to diagnose painful TMD with a sensitivity of 58.8% and specificity of 69.3%. The diagnostic ability of the visual analysis for diagnosing painful TMD was not significantly different from that of the quantitative analysis.Visual bone scintigraphic analysis showed a diagnostic utility similar to that of quantitative assessment for the diagnosis of painful TMD.

We present a novel approach for computing link-based similarities among objects accurately by utilizing the link information pertaining to the objects involved. We discuss the problems with previous link-based similarity measures and propose a novel approach for computing link based similarities that does not suffer from these problems. In the proposed approach each target object is represented by a vector. Each element of the vector corresponds to all the objects in the given data, and the value of each element denotes the weight for the corresponding object. As for this weight value, we propose to utilize the probability of reaching from the target object to the specific object, computed using the "Random Walk with Restart" strategy. Then, we define the similarity between two objects as the cosine similarity of the two vectors. In this paper, we provide examples to show that our approach does not suffer from the aforementioned problems. We also evaluate the performance of the proposed methods in comparison with existing link-based measures, qualitatively and quantitatively, with respect to two kinds of data sets, scientific papers and Web documents. Our experimental results indicate that the proposed methods significantly outperform the existing measures.