The Relationship between Ischemic Stroke Patients with and without Retroflex Tongue: A Retrospective Study

Background. Patients suffering from stroke exhibit different levels of capability in retroflex tongues, in our clinical observation. This study aims to derive the association of tongue retroflexibility with the degree of severity for stroke patients. Methods. All ischemic stroke patients were collected from August 2010 to July 2013 in the Stroke Center, Changhua Christian Hospital, Taiwan. All participants underwent medical history collection and clinical examination, including tongue images captured by ATDS. Statistical analysis was performed to compare the differences of ischemic stroke patients with and without retroflex tongue. Result. Among the total of 308 cases collected, 123 patients cannot retroflex their tongues, that is, the non-RT group. The length of stay in the non-RT group, 32.0 ± 21.5, was longer than those of the RT counterparts, 25.9 ± 14.4 (p value: 0.007). The NIHSS on admission, 14.1 ± 7.8 versus 8.9 ± 5.2, was higher and the Barthel Index upon admission, 18.6 ± 20.7 and 35.0 ± 24.2, was lower for the non-RT patients than that of the RT counterparts. Also, the non-RT patients account for 60.2% and 75.6% for Barthel Index ≤ 17 and NIHSS ≥ 9, respectively. Conclusion. The stroke patients in non-RT group showed significantly poor prognosis and were more serious in the degree of severity and level of autonomy than RT group, indicating that the ability to maneuver tongue retroflex can serve as a simple, reliable, and noninvasive means for the prognosis of ischemic stroke patients

Quantification of tongue colour using machine learning in Kampo medicine

AbstractIntroductionThe evaluation of tongue colour has been an important approach to examine human health in Kampo medicine (traditional Japanese medicine) because the change in tongue colour may suggest physical or mental disorders. Several tongue colour quantification methods have been published to objectify clinical information among East Asian countries. However, reliable tongue colour analysis results among Japanese test persons are limited because of a lack of quantitative evaluation of tongue colour. We aimed to use advances in digital imaging processing to quantify and verify clinical data tongue colour diagnosis by characterising differences intongue features.MethodsThe DS01-B tongue colour information acquisition system was used to extract tongue images of 1080 Japanese test subjects. Evaluation of tongue colour, body and coating was performed by 10 experienced Kampo medicine physicians. The acquired images were classified into five tongue body colour categories and six tongue coating colour categories based on evaluations from 10 physicians with extensive Kampo medicine experience. K-means clustering algorithm was applied as a machine learning (the study of pattern recognition by computational learning) method to the acquired images to quantify tongue body and coating colour information.ResultsTongue body (n=550) and tongue coating (n=516) colour samples were classified and analysed. Clusters consisting of five tongue body colour categories and six tongue coating colour categories were experimentally described in the CIELAB colour space. Statistical differences were evident among the clinically primary tongue colours.ConclusionsClinically important tongue colour differences in Kampo medicine can be visualised by applying machine learning to tongue images taken under stable conditions. This has implications for developing globally unified, reliable tongue colour diagnostic criteria which could be used to explore the relevance between clinical status and tongue colour

The bi-elliptical deformable contour and its application to automated tongue segmentation in Chinese medicine

2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Multi-modal classifier fusion with feature cooperation for glaucoma diagnosis

Background: Glaucoma is a major public health problem that can lead to an optic nerve lesion, requiring systematic screening in the population over 45 years of age. The diagnosis and classification of this disease have had a marked and excellent development in recent years, particularly in the machine learning domain. Multimodal data have been shown to be a significant aid to the machine learning domain, especially by its contribution to improving data driven decision-making. Method: Solving classification problems by combinations of classifiers has made it possible to increase the robustness as well as the classification reliability by using the complementarity that may exist between the classifiers. Complementarity is considered a key property of multimodality. A Convolutional Neural Network (CNN) works very well in pattern recognition and has been shown to exhibit superior performance, especially for image classification which can learn by themselves useful features from raw data. This article proposes a multimodal classification approach based on deep Convolutional Neural Network and Support Vector Machine (SVM) classifiers using multimodal data and multimodal feature for glaucoma diagnosis from retinal fundus images from RIM-ONE dataset. We make use of handcrafted feature descriptors such as the Gray Level Co-Occurrence Matrix, Central Moments and Hu Moments to co-operate with features automatically generated by the CNN in order to properly detect the optic nerve and consequently obtain a better classification rate, allowing a more reliable diagnosis of glaucoma. Results: The experimental results confirm that the combination of classifiers using the BWWV technique is better than learning classifiers separately. The proposed method provides a computerized diagnosis system for glaucoma disease with impressive results comparing them to the main related studies that allow us to continue in this research path