Advances in automated tongue diagnosis techniques

This paper reviews the recent advances in a significant constituent of traditional oriental medicinal technology, called tongue diagnosis. Tongue diagnosis can be an effective, noninvasive method to perform an auxiliary diagnosis any time anywhere, which can support the global need in the primary healthcare system. This work explores the literature to evaluate the works done on the various aspects of computerized tongue diagnosis, namely preprocessing, tongue detection, segmentation, feature extraction, tongue analysis, especially in traditional Chinese medicine (TCM). In spite of huge volume of work done on automatic tongue diagnosis (ATD), there is a lack of adequate survey, especially to combine it with the current diagnosis trends. This paper studies the merits, capabilities, and associated research gaps in current works on ATD systems. After exploring the algorithms used in tongue diagnosis, the current trend and global requirements in health domain motivates us to propose a conceptual framework for the automated tongue diagnostic system on mobile enabled platform. This framework will be able to connect tongue diagnosis with the future point-of-care health system

Artificial Intelligence in Oral Health

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others

Computerized tongue diagnosis based on Bayesian networks

2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Deep Machine Learning for Oral Cancer : From Precise Diagnosis to Precision Medicine

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.© 2022 Alabi, Almangush, Elmusrati and Mäkitie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.fi=vertaisarvioitu|en=peerReviewed

Review on the current trends in tongue diagnosis systems

AbstractTongue diagnosis is an essential process to noninvasively assess the condition of a patient's internal organs in traditional medicine. To obtain quantitative and objective diagnostic results, image acquisition and analysis devices called tongue diagnosis systems (TDSs) are required. These systems consist of hardware including cameras, light sources, and a ColorChecker, and software for color correction, segmentation of tongue region, and tongue classification. To improve the performance of TDSs, various types TDSs have been developed. Hyperspectral imaging TDSs have been suggested to acquire more information than a two-dimensional (2D) image with visible light waves, as it allows collection of data from multiple bands. Three-dimensional (3D) imaging TDSs have been suggested to provide 3D geometry. In the near future, mobile devices like the smart phone will offer applications for assessment of health condition using tongue images. Various technologies for the TDS have respective unique advantages and specificities according to the application and diagnostic environment, but this variation may cause inconsistent diagnoses in practical clinical applications. In this manuscript, we reviewed the current trends in TDSs for the standardization of systems. In conclusion, the standardization of TDSs can supply the general public and oriental medical doctors with convenient, prompt, and accurate information with diagnostic results for assessing the health condition

Analysis of Tongue Color-Associated Features among Patients with PCR-Confirmed COVID-19 Infection in Ukraine

Objective: To evaluate and systematize tongue color-related manifestations among patients with PCR-confirmed COVID-19 infection. Material and Methods: This retrospective study included analysis of tongue images obtained from patients with PCR-confirmed COVID-19 infection. Evaluation of coronavirus disease severity (mild, moderate, severe, critical) was provided, considering clinical symptomatology and results of laboratorial and instrumental diagnostic methods. Each picture was analyzed considering the parameters of color of the tongue and color of the tongue plaque by two dental specialists. Cochran-Armitage test for trend was used to evaluate associations between the tongue color and tongue plaque color, and coronavirus disease severity. Results: The most prevalent tongue colors were pale pink, red and dark red (burgundy color). A total of 64.29% of patients with mild disease demonstrated pale pink color of the tongue. Patients with moderate coronavirus disease were characterized with the adverse trend: 62.35% of them presented with red-colored tongue, while in 37.64% of cases, the tongue was pale pink. Severe COVID-19 patients, almost in 90% of the cases, had either red or burgundy color of the tongue. Conclusion: SARS-COV-2 infection is not manifested by tongue-targeted or tongue-specific signs and features; however, coronavirus disease itself provokes changes within the tongue color and tongue plaque color similar to those registered during other internal pathologies