Testing the accuracy of autofluorescence device in diagnosing oral potentially malignant disorders among people with HIV seeking dental care.

OBJECTIVES: Although many studies have assessed the diagnostic accuracy of autofluorescence in oral potentially malignant disorders (OPMDs), there has been a paucity of such information in high-risk populations. Our study thereby tested the accuracy of using autofluorescence in the oral examination of suspicious lesions among patients seeking care at an HIV-specialized dental clinic in Houston, Texas. MATERIALS AND METHODS: We performed a prospective single-arm design in which forty-four (44) HIV-infected individuals seeking dental care at a specialized-HIV dental clinic were recruited. Each subject had their oral cavity examined under conventional lighting and then used a fluorescence light-based handheld device (OralID®). Biopsy was obtained from unresolved suspicious OPMDs at the 15-day follow-up, and histopathological analysis was conducted. The oral lesions, not the patient, were treated as the unit of analysis. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were calculated using SPSS. RESULTS: The results showed that OPMDs could be identified with a sensitivity of 90%, a specificity of 18%, an NPV of 86%, a PPV of 24% using the fluorescence light-based handheld device, with a diagnostic accuracy of 55%. CONCLUSIONS: Despite the low specificity, fluorescence light can complement clinical oral cancer screening and aid identification of OPMDs during biopsy procedures. CLINICAL RELEVANCE: Our findings suggest that autofluorescence devices could supplement clinical oral examination and aid the identification of OPMDs during biopsy procedures, potentially improving oral cancer screening among HIV-positive patients seeking care.

Fourier Transform Infrared Spectroscopy in Oral Cancer Diagnosis.

Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.

Clinical validation of a saliva-based matrix metalloproteinase-1 rapid strip test for detection of oral cavity cancer.

BACKGROUND: We previously identified matrix metalloproteinase-1 (MMP-1) as one of the most promising salivary biomarkers for oral squamous cell carcinoma (OSCC) and developed a sensitive ELISA for MMP-1 with good performance in detection of OSCC using a cohort of 1160 saliva samples. METHODS: A time-saving rapid strip test (RST) for MMP-1 was developed in this study and its diagnostic performance compared with ELISA using saliva samples from a new cohort of 603 subjects (171 healthy controls, 236 patients with oral potentially malignant disorders, and 196 OSCC patients). RESULTS: Salivary MMP-1 levels measured using RST and ELISA were highly comparable and both assays could effectively distinguish between OSCC and non-cancerous groups. Similar to ELISA, receiver operating characteristic curve analysis of the MMP-1 RST was effective in identifying patients with OSCC at different oral cavity sites and stages. CONCLUSIONS: Salivary MMP-1 can be sensitively detected using both RST and ELISA methods. Our newly developed point-of-care MMP-1 RST is a promising in vitro diagnostic device (IVD) that may serve as a novel auxiliary tool in the routine clinical detection and monitoring of OSCC.

A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions.

Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people's lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI's drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.

The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer.

Oral cancer (OC) is the sixth most commonly reported malignant disease globally, with high rates of disease-related morbidity and mortality due to advanced loco-regional stage at diagnosis. Early detection and prompt treatment offer the best outcomes to patients, yet the majority of OC lesions are detected at late stages with 45% survival rate for 2 years. The primary cause of poor OC outcomes is unavailable or ineffective screening and surveillance at the local point-of-care level, leading to delays in specialist referral and subsequent treatment. Lack of adequate awareness of OC among the public and professionals, and barriers to accessing health care services in a timely manner also contribute to delayed diagnosis. As image analysis and diagnostic technologies are evolving, various artificial intelligence (AI) approaches, specific algorithms and predictive models are beginning to have a considerable impact in improving diagnostic accuracy for OC. AI based technologies combined with intraoral photographic images or optical imaging methods are under investigation for automated detection and classification of OC. These new methods and technologies have great potential to improve outcomes, especially in low-resource settings. Such approaches can be used to predict oral cancer risk as an adjunct to population screening by providing real-time risk assessment. The objective of this study is to (1) provide an overview of components of delayed OC diagnosis and (2) evaluate novel AI based approaches with respect to their utility and implications for improving oral cancer detection.

Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review.

Oral cancer (OC) is a deadly disease with a high mortality and complex etiology. Artificial intelligence (AI) is one of the outstanding innovations in technology used in dental science. This paper intends to report on the application and performance of AI in diagnosis and predicting the occurrence of OC. In this study, we carried out data search through an electronic search in several renowned databases, which mainly included PubMed, Google Scholar, Scopus, Embase, Cochrane, Web of Science, and the Saudi Digital Library for articles that were published between January 2000 to March 2021. We included 16 articles that met the eligibility criteria and were critically analyzed using QUADAS-2. AI can precisely analyze an enormous dataset of images (fluorescent, hyperspectral, cytology, CT images, etc.) to diagnose OC. AI can accurately predict the occurrence of OC, as compared to conventional methods, by analyzing predisposing factors like age, gender, tobacco habits, and bio-markers. The precision and accuracy of AI in diagnosis as well as predicting the occurrence are higher than the current, existing clinical strategies, as well as conventional statistics like cox regression analysis and logistic regression.

Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis.

SIGNIFICANCE: Oral cancer is a quite common global health issue. Early diagnosis of cancerous and potentially malignant disorders in the oral cavity would significantly increase the survival rate of oral cancer. Previously reported smartphone-based images detection methods for oral cancer mainly focus on demonstrating the effectiveness of their methodology, yet it still lacks systematic study on how to improve the diagnosis accuracy on oral disease using hand-held smartphone photographic images. AIM: We present an effective smartphone-based imaging diagnosis method, powered by a deep learning algorithm, to address the challenges of automatic detection of oral diseases. APPROACH: We conducted a retrospective study. First, a simple yet effective centered rule image-capturing approach was proposed for collecting oral cavity images. Then, based on this method, a medium-sized oral dataset with five categories of diseases was created, and a resampling method was presented to alleviate the effect of image variability from hand-held smartphone cameras. Finally, a recent deep learning network (HRNet) was introduced to evaluate the performance of our method for oral cancer detection. RESULTS: The performance of the proposed method achieved a sensitivity of 83.0%, specificity of 96.6%, precision of 84.3%, and F1 of 83.6% on 455 test images. The proposed "center positioning" method was about 8% higher than that of a simulated "random positioning" method in terms of F1 score, the resampling method had additional 6% of performance improvement, and the introduced HRNet achieved slightly better performance than VGG16, ResNet50, and DenseNet169, with respect to the metrics of sensitivity, specificity, precision, and F1. CONCLUSIONS: Capturing oral images centered on the lesion, resampling the cases in training set, and using the HRNet can effectively improve the performance of deep learning algorithm on oral cancer detection. The smartphone-based imaging with deep learning method has good potential for primary oral cancer diagnosis.

Spectroscopic Studies on the Biomolecular Recognition of Toluidine Blue: Key Information Towards Development of a Non-Contact, Non-Invasive Device for Oral Cancer Detection.

Molecular interaction of aromatic dyes with biological macromolecules are important for the development of minimally invasive disease diagnostic biotechnologies. In the present work, we have used Toluidine Blue (TB) as a model dye, which is a well-known staining agent for the diagnosis of oral cancer and have studied the interaction of various biological macromolecules (protein and DNA) with the dye at different pH. Our spectroscopic studies confirm that TB interacts with Human Serum Albumin (HSA), a model protein at very high pH conditions which is very hard to achieve physiologically. On the other hand, TB significantly interacts with the DNA at physiological pH value (7.4). Our molecular studies strengthen the understanding of the Toluidine Blue staining of cancer cells, where the relative ratio of the nucleic acids is higher than the normal intracellular content. We have also developed a non-invasive, non-contact spectroscopic technique to explore the possibility of quantitatively detecting oral cancer by exploiting the interaction of TB with DNA. We have also reported development of a prototype named "Oral-O-Scope" for the detection of Oral cancer and have carried out human studies using the prototype.

Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm.

PURPOSE: Oral cancer is a complex wide spread cancer, which has high severity. Using advanced technology and deep learning algorithm early detection and classification are made possible. Medical imaging technique, computer-aided diagnosis and detection can make potential changes in cancer treatment. In this research work, we have developed a deep learning algorithm for automated, computer-aided oral cancer detecting system by investigating patient hyperspectral images. METHODS: To validate the proposed regression-based partitioned deep learning algorithm, we compare the performance with other techniques by its classification accuracy, specificity, and sensitivity. For the accurate medical image classification objective, we demonstrate a new structure of partitioned deep Convolution Neural Network (CNN) with two partitioned layers for labeling and classify by labeling region of interest in multidimensional hyperspectral image. RESULTS: The performance of the partitioned deep CNN was verified by classification accuracy. We have obtained classification accuracy of 91.4% with sensitivity 0.94 and a specificity of 0.91 for 100 image data sets training for task classification of cancerous tumor with benign and for task classification of cancerous tumor with normal tissue accuracy of 94.5% for 500 training patterns was obtained. CONCLUSIONS: We compared the obtained results from another traditional medical image classification algorithm. From the obtained result, we identify that the quality of diagnosis is increased by proposed regression-based partitioned CNN learning algorithm for a complex medical image of oral cancer diagnosis.