The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa

Deep learning in oral cancer- a systematic review

BACKGROUND

Oral cancer is a life-threatening malignancy, which affects the survival rate and quality of life of patients. The aim of this systematic review was to review deep learning (DL) studies in the diagnosis and prognostic prediction of oral cancer.

METHODS

This systematic review was conducted following the PRISMA guidelines. Databases (Medline via PubMed, Google Scholar, Scopus) were searched for relevant studies, from January 2000 to June 2023.

RESULTS

Fifty-four qualified for inclusion, including diagnostic (n = 51), and prognostic prediction (n = 3). Thirteen studies showed a low risk of biases in all domains, and 40 studies low risk for concerns regarding applicability. The performance of DL models was reported of the accuracy of 85.0-100%, F1-score of 79.31 - 89.0%, Dice coefficient index of 76.0 - 96.3% and Concordance index of 0.78-0.95 for classification, object detection, segmentation, and prognostic prediction, respectively. The pooled diagnostic odds ratios were 2549.08 (95% CI 410.77-4687.39) for classification studies.

CONCLUSIONS

The number of DL studies in oral cancer is increasing, with a diverse type of architectures. The reported accuracy showed promising DL performance in studies of oral cancer and appeared to have potential utility in improving informed clinical decision-making of oral cancer.

Optical coherence tomography: Promising imaging technique for the diagnosis of oral mucosal diseases

OBJECTIVE

This review aims to summarize the latest application of optical coherence tomography (OCT) in oral mucosal diseases, promoting an accurate and earlier diagnosis of such disorders, which are difficult to be differentiated.

SUBJECTIVE AND METHODS

References on the application of OCT in oral mucosal diseases were mainly obtained from PubMed, Embase, Web of Science and Scopus databases, using the keywords: "optical coherence tomography and 'oral mucosa/oral cancers/oral potentially malignant diseases/oral lichen planus/oral leukoplakia/oral erythroplakia/discoid lupus erythematosus/oral autoimmune bullous diseases/oral ulcers/erythema multiforme/oral mucositis'".

RESULTS

It is found that OCT is showing a promising application potential in the early detection, diagnosis, differential diagnosis, monitoring of oral cancer and oral dysplastic lesions, as well as the delineation of tumor margins. OCT is also playing an increasingly important role in the diagnosis of oral potentially malignant disorders, oral mucosal bullous diseases, oral ulcerative diseases, erythema multiforme, and the early detection of oral mucositis.

CONCLUSION

Optical coherence tomography, as a novel optical technique featured by real-time, noninvasive, dynamic and high-resolution imaging, is of great use to serve as an adjunct tool for the diagnosis, differential diagnosis, monitoring and therapy evaluation of oral mucosal diseases.

Artificial intelligence in dentistry—A review

Artificial Intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence. AI is not a new term, the concept of AI can be dated back to 1950. However, it has not become a practical tool until two decades ago. Owing to the rapid development of three cornerstones of current AI technology—big data (coming through digital devices), computational power, and AI algorithm—in the past two decades, AI applications have been started to provide convenience to people's lives. In dentistry, AI has been adopted in all dental disciplines, i.e., operative dentistry, periodontics, orthodontics, oral and maxillofacial surgery, and prosthodontics. The majority of the AI applications in dentistry go to the diagnosis based on radiographic or optical images, while other tasks are not as applicable as image-based tasks mainly due to the constraints of data availability, data uniformity, and computational power for handling 3D data. Evidence-based dentistry (EBD) is regarded as the gold standard for the decision-making of dental professionals, while AI machine learning (ML) models learn from human expertise. ML can be seen as another valuable tool to assist dental professionals in multiple stages of clinical cases. This review narrated the history and classification of AI, summarised AI applications in dentistry, discussed the relationship between EBD and ML, and aimed to help dental professionals to understand AI as a tool better to assist their routine work with improved efficiency.

Efficacy of optical coherence tomography in the diagnosing of oral cancerous lesion: systematic review and meta-analysis

Non-invasive diagnostic tools that facilitate visualization of potentially malignant oral lesions and cancers have been introduced. Oral lesions detected by optical coherence tomography (OCT) were compared to reference results based on histological findings. The diagnostic odds ratio (DOR), along with summary receiver operating characteristic curve (SROC), area under SROC, sensitivity, specificity, and negative predictive values, were the outcomes. The DOR of OCT was 86.9190 (95% confidence interval [CI]: 38.7435, 194.9985), and the area under SROC was 0.951. OCT showed good sensitivity (0.9138; 95% CI: 0.8758, 0.9409) and specificity (0.9110; 95% CI: 0.8568, 0.9460), and a high negative predictive value (0.9225; 95% CI: 0.8863, 0.9478). Diagnostic sensitivity was higher when using artificial intelligence and automated algorithms compared to diagnoses made by clinicians. OCT is non-invasive, provides rapid results without radiation exposure, and can aid in the diagnosis and follow-up of oral cancer and oral precancerous lesions.

In vivo evaluation of traumatic and malignant oral ulcers with optical coherence tomography: A comparison between histopathological and ultrastructural findings

Ulcers in the oral mucosa is a relatively common, although challenging, entity in oral medicine, as it can arise due to a wide range of traumatic, infective, autoimmune, and neoplastic disorders. Although histopathology of lesional and peri‑lesional tissues remains the gold standard for persistent oral breaching, optical coherence tomography (OCT) has been recently suggested as a potential ally to enhance the early or non-invasive diagnosis of likely causation. The aim of the present study was to provide an in-vivo OCT analysis and description from a sample of 70 patients affected by traumatic or neoplastic-related ulcers, located on the buccal mucosa, tongue or gingiva, and compare the OCT data with those of 20 patients with healthy oral mucosa. OCT dynamic scans revealed clear distinction of epithelial layer (EP), lamina propria (LP) of healthy buccal mucosa, gingiva, and tongue as well as allowing observation of the keratin layer in gingiva, and the subepithelial vascularization of each site. Traumatic lesions had an EP of reduced in thickness, with an irregular, if not disrupted surface. Interestingly, LP seemed to preserve its reflectiveness and vascularization only in the traumatic lesions. Among neoplastic lesions, regardless their site of onset, both EP integrity/homogeneity, and LP reflectiveness/vascularization were lost and unrecognizable when compared to their healthy counterparts. OCT scanning allowed some differentiation between traumatic and malignant ulcers and thus may a useful and non-invasive means of determining the need and/or urgency of histopathological examination of oral lesions.

Oral Cancer Screening by Artificial Intelligence-Oriented Interpretation of Optical Coherence Tomography Images

Early diagnosis of oral cancer is critical to improve the survival rate of patients. The current strategies for screening of patients for oral premalignant and malignant lesions unfortunately miss a significant number of involved patients. Optical coherence tomography (OCT) is an optical imaging modality that has been widely investigated in the field of oncology for identification of cancerous entities. Since the interpretation of OCT images requires professional training and OCT images contain information that cannot be inferred visually, artificial intelligence (AI) with trained algorithms has the ability to quantify visually undetectable variations, thus overcoming the barriers that have postponed the involvement of OCT in the process of screening of oral neoplastic lesions. This literature review aimed to highlight the features of precancerous and cancerous oral lesions on OCT images and specify how AI can assist in screening and diagnosis of such pathologies.

Detecting Oral Cancer: The Potential of Artificial Intelligence

BACKGROUND

Physical inspection is a simple way to diagnose oral cancer. Most cases of oral cancer, on the contrary, are diagnosed late, resulting in needless mortality and morbidity. While screening high-risk populations appear to be helpful, these people are often found in areas with minimal access to health care. In this paper, we have reviewed several aspects related to oral cancer such as its cause, the risk factors associated with it, India's oral cancer situation at the moment, various screening methods, and the ability of artificial intelligence in the detection and classification purpose. Oral cancer results can be enhanced by combining imaging and artificial intelligence approaches for better detection and diagnosis.

OBJECTIVE

This paper aims to cover the various oral cancer screening detection techniques that use Artificial Intelligence (AI).

METHODS

In this paper, we have covered the imaging methods that are used in screening oral cancer and after that the potential of AI for the detection of oral cancer.

CONCLUSION

This paper covers some of the main concepts regarding oral cancer and various AI methods used to detect it.

Validation of a Point-of-Care Optical Coherence Tomography Device with Machine Learning Algorithm for Detection of Oral Potentially Malignant and Malignant Lesions

Non-invasive strategies that can identify oral malignant and dysplastic oral potentially-malignant lesions (OPML) are necessary in cancer screening and long-term surveillance. Optical coherence tomography (OCT) can be a rapid, real time and non-invasive imaging method for frequent patient surveillance. Here, we report the validation of a portable, robust OCT device in 232 patients (lesions: 347) in different clinical settings. The device deployed with algorithm-based automated diagnosis, showed efficacy in delineation of oral benign and normal (n = 151), OPML (n = 121), and malignant lesions (n = 75) in community and tertiary care settings. This study showed that OCT images analyzed by automated image processing algorithm could distinguish the dysplastic-OPML and malignant lesions with a sensitivity of 95% and 93%, respectively. Furthermore, we explored the ability of multiple (n = 14) artificial neural network (ANN) based feature extraction techniques for delineation high grade-OPML (moderate/severe dysplasia). The support vector machine (SVM) model built over ANN, delineated high-grade dysplasia with sensitivity of 83%, which in turn, can be employed to triage patients for tertiary care. The study provides evidence towards the utility of the robust and low-cost OCT instrument as a point-of-care device in resource-constrained settings and the potential clinical application of device in screening and surveillance of oral cancer.

Imaging-based navigation technologies in head and neck surgery

PURPOSE OF REVIEW

Image guided navigation has had significant impact in head and neck surgery, and has been most prolific in endonasal surgeries. Although conventional image guidance involves static computed tomography (CT) images attained in the preoperative setting, the continual evolution of surgical navigation technologies is fast expanding to incorporate both real-time data and bioinformation that allows for improved precision in surgical guidance. With the rapid advances in technologies, this article allows for a timely review of the current and developing techniques in surgical navigation for head and neck surgery.

RECENT FINDINGS

Current advances for cross-sectional-based image-guided surgery include fusion of CT with other imaging modalities (e.g., magnetic resonance imaging and positron emission tomography) as well as the uptake in intraoperative real-time 'on the table' imaging (e.g., cone-beam CT). These advances, together with the integration of virtual/augmented reality, enable potential enhancements in surgical navigation. In addition to the advances in radiological imaging, the development of optical modalities such as fluorescence and spectroscopy techniques further allows the assimilation of biological data to improve navigation particularly for head and neck surgery.

SUMMARY

The steady development of radiological and optical imaging techniques shows great promise in changing the paradigm of head and neck surgery.

Application of optical coherence tomography to study the structural features of oral mucosa in biopsy tissues of oral dysplasia and carcinomas

OBJECTIVE

This study aims to examine the ability of optical coherence tomography (OCT) to differentiate ex vivo epithelial structure of benign disorders, dysplastic, and oral squamous cell carcinoma (OSCC) in comparison with the structure of normal marginal mucosa of oral biopsies. As a secondary objective, we examined the inter- and intra-observer variations of OCT measurements of two calibrated assessors.

MATERIALS AND METHODS

Oral biopsies (n = 44) were scanned using the swept source OCT (SSOCT) and grouped by pathology diagnosis to benign, dysplasia or carcinoma. Two trained and calibrated assessors scored on the five OCT variables: thickness of keratin layer (KL), epithelial layer (EL), homogeneity of lamina propria (LP), basement membrane integrity (BMI), and the degree of reflection of the epithelial layer (Ep Re). Chi-square tests and Fischer's exact method were used to compare the data.

RESULTS

The OCT images showed breached BM status in all the OSCC samples (100%). Epithelial reflection was noted to be hyper-reflective in all the OSCC and oral dysplasia samples (100%). An increase in KL in 66.67% of the OSCC and 100% of the oral dysplasia samples was found. EL was increased in all the OSCC samples (100%) and 85.72% of the oral dysplasias. Kappa values showed that there was very good agreement (over 0.7) when scoring individual parameters between the two assessors.

CONCLUSION

The study showed that the BM status was a key parameter in the detection of SCC and for differentiating SCC from oral dysplasia or benign disorders.

CLINICAL RELEVANCE

OCT is a non-invasive and non-radioactive adjunct diagnostic tool that can provide immediate results on the structure of oral mucosa. The BM status measured ex vivo was a key parameter in the detection of SCC and for differentiating SCC from oral dysplasia or benign disorders.