Texture-Map-Based Branch-Collaborative Network for Oral Cancer Detection

Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis

Objective

Oral cancer is a widespread global health problem characterised by high mortality rates, wherein early detection is critical for better survival outcomes and quality of life. While visual examination is the primary method for detecting oral cancer, it may not be practical in remote areas. AI algorithms have shown some promise in detecting cancer from medical images, but their effectiveness in oral cancer detection remains Naïve. This systematic review aims to provide an extensive assessment of the existing evidence about the diagnostic accuracy of AI-driven approaches for detecting oral potentially malignant disorders (OPMDs) and oral cancer using medical diagnostic imaging.

Methods

Adhering to PRISMA guidelines, the review scrutinised literature from PubMed, Scopus, and IEEE databases, with a specific focus on evaluating the performance of AI architectures across diverse imaging modalities for the detection of these conditions.

Results

The performance of AI models, measured by sensitivity and specificity, was assessed using a hierarchical summary receiver operating characteristic (SROC) curve, with heterogeneity quantified through I2 statistic. To account for inter-study variability, a random effects model was utilized. We screened 296 articles, included 55 studies for qualitative synthesis, and selected 18 studies for meta-analysis. Studies evaluating the diagnostic efficacy of AI-based methods reveal a high sensitivity of 0.87 and specificity of 0.81. The diagnostic odds ratio (DOR) of 131.63 indicates a high likelihood of accurate diagnosis of oral cancer and OPMDs. The SROC curve (AUC) of 0.9758 indicates the exceptional diagnostic performance of such models. The research showed that deep learning (DL) architectures, especially CNNs (convolutional neural networks), were the best at finding OPMDs and oral cancer. Histopathological images exhibited the greatest sensitivity and specificity in these detections.

Conclusion

These findings suggest that AI algorithms have the potential to function as reliable tools for the early diagnosis of OPMDs and oral cancer, offering significant advantages, particularly in resource-constrained settings.

Systematic Review Registration

https://www.crd.york.ac.uk/, PROSPERO (CRD42023476706).

Artificial Intelligence in Head and Neck Surgery

This article explores artificial intelligence's (AI's) role in otolaryngology for head and neck cancer diagnosis and management. It highlights AI's potential in pattern recognition for early cancer detection, prognostication, and treatment planning, primarily through image analysis using clinical, endoscopic, and histopathologic images. Radiomics is also discussed at length, as well as the many ways that radiologic image analysis can be utilized, including for diagnosis, lymph node metastasis prediction, and evaluation of treatment response. The study highlights AI's promise and limitations, underlining the need for clinician-data scientist collaboration to enhance head and neck cancer care.

Illuminating the future of precision cancer surgery with fluorescence imaging and artificial intelligence convergence

Real-time and accurate guidance for tumor resection has long been anticipated by surgeons. In the past decade, the flourishing material science has made impressive progress in near-infrared fluorophores that may fulfill this purpose. Fluorescence imaging-guided surgery shows great promise for clinical application and has undergone widespread evaluations, though it still requires continuous improvements to transition this technique from bench to bedside. Concurrently, the rapid progress of artificial intelligence (AI) has revolutionized medicine, aiding in the screening, diagnosis, and treatment of human doctors. Incorporating AI helps enhance fluorescence imaging and is poised to bring major innovations to surgical guidance, thereby realizing precision cancer surgery. This review provides an overview of the principles and clinical evaluations of fluorescence-guided surgery. Furthermore, recent endeavors to synergize AI with fluorescence imaging were presented, and the benefits of this interdisciplinary convergence were discussed. Finally, several implementation strategies to overcome technical hurdles were proposed to encourage and inspire future research to expedite the clinical application of these revolutionary technologies.

Application of deep learning radiomics in oral squamous cell carcinoma-Extracting more information from medical images using advanced feature analysis

OBJECTIVE

To conduct a systematic review with meta-analyses to assess the recent scientific literature addressing the application of deep learning radiomics in oral squamous cell carcinoma (OSCC).

MATERIALS AND METHODS

Electronic and manual literature retrieval was performed using PubMed, Web of Science, EMbase, Ovid-MEDLINE, and IEEE databases from 2012 to 2023. The ROBINS-I tool was used for quality evaluation; random-effects model was used; and results were reported according to the PRISMA statement.

RESULTS

A total of 26 studies involving 64,731 medical images were included in quantitative synthesis. The meta-analysis showed that, the pooled sensitivity and specificity were 0.88 (95 %CI: 0.87∼0.88) and 0.80 (95 %CI: 0.80∼0.81), respectively. Deeks' asymmetry test revealed there existed slight publication bias (P = 0.03).

CONCLUSIONS

The advances in the application of radiomics combined with learning algorithm in OSCC were reviewed, including diagnosis and differential diagnosis of OSCC, efficacy assessment and prognosis prediction. The demerits of deep learning radiomics at the current stage and its future development direction aimed at medical imaging diagnosis were also summarized and analyzed at the end of the article.

Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis

OBJECTIVE

This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs.

MATERIALS AND METHOD

Inclusion criteria were (1) studies employing AI to (2) classify, detect, or segment oral mucosa lesions, (3) on oral photographs of human subjects. Included studies were assessed for risk of bias using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A PubMed, Scopus, Embase, Web of Science, IEEE, arXiv, medRxiv, and grey literature (Google Scholar) search was conducted until June 2023, without language limitation.

RESULTS

After initial searching, 36 eligible studies (from 8734 identified records) were included. Based on QUADAS-2, only 7% of studies were at low risk of bias for all domains. Studies employed different AI models and reported a wide range of outcomes and metrics. The accuracy of AI for detecting oral mucosal lesions ranged from 74 to 100%, while that for clinicians un-aided by AI ranged from 61 to 98%. Pooled diagnostic odds ratio for studies which evaluated AI for diagnosing or discriminating potentially malignant lesions was 155 (95% confidence interval 23-1019), while that for cancerous lesions was 114 (59-221).

CONCLUSIONS

AI may assist in oral mucosa lesion screening while the expected accuracy gains or further health benefits remain unclear so far.

CLINICAL RELEVANCE

Artificial intelligence assists oral mucosa lesion screening and may foster more targeted testing and referral in the hands of non-specialist providers, for example. So far, it remains unclear if accuracy gains compared with specialized can be realized.

Intelligent deep learning supports biomedical image detection and classification of oral cancer

BACKGROUND

Oral cancer is a malignant tumor that usually occurs within the tissues of the mouth. This type of cancer mainly includes tumors in the lining of the mouth, tongue, lips, buccal mucosa and gums. Oral cancer is on the rise globally, especially in some specific risk groups. The early stage of oral cancer is usually asymptomatic, while the late stage may present with ulcers, lumps, bleeding, etc.

OBJECTIVE

The objective of this paper is to propose an effective and accurate method for the identification and classification of oral cancer.

METHODS

We applied two deep learning methods, CNN and Transformers. First, we propose a new CANet classification model for oral cancer, which uses attention mechanisms combined with neglected location information to explore the complex combination of attention mechanisms and deep networks, and fully tap the potential of attention mechanisms. Secondly, we design a classification model based on Swim transform. The image is segmented into a series of two-dimensional image blocks, which are then processed by multiple layers of conversion blocks.

RESULTS

The proposed classification model was trained and predicted on Kaggle Oral Cancer Images Dataset, and satisfactory results were obtained. The average accuracy, sensitivity, specificity and F1-Socre of Swin transformer architecture are 94.95%, 95.37%, 95.52% and 94.66%, respectively. The average accuracy, sensitivity, specificity and F1-Score of CANet model were 97.00%, 97.82%, 97.82% and 96.61%, respectively.

CONCLUSIONS

We studied different deep learning algorithms for oral cancer classification, including convolutional neural networks, converters, etc. Our Attention module in CANet leverages the benefits of channel attention to model the relationships between channels while encoding precise location information that captures the long-term dependencies of the network. The model achieves a high classification effect with an accuracy of 97.00%, which can be used in the automatic recognition and classification of oral cancer.

Multi-Method Analysis of Histopathological Image for Early Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning and Hybrid Techniques

Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient's chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.

Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract

PROBLEM

Artificial intelligence has been widely investigated for diagnosis and treatment strategy design, with some models proposed for detecting oral pharyngeal, nasopharyngeal, or laryngeal carcinoma. However, no comprehensive model has been established for these regions.

AIM

Our hypothesis was that a common pattern in the cancerous appearance of these regions could be recognized and integrated into a single model, thus improving the efficacy of deep learning models.

METHODS

We utilized a point-wise spatial attention network model to perform semantic segmentation in these regions.

RESULTS

Our study demonstrated an excellent outcome, with an average mIoU of 86.3%, and an average pixel accuracy of 96.3%.

CONCLUSION

The research confirmed that the mucosa of oral pharyngeal, nasopharyngeal, and laryngeal regions may share a common appearance, including the appearance of tumors, which can be recognized by a single artificial intelligence model. Therefore, a deep learning model could be constructed to effectively recognize these tumors.

Integrating Cutting-Edge Methods to Oral Cancer Screening, Analysis, and Prognosis

Oral cancer (OC) has become a significant barrier to health worldwide due to its high morbidity and mortality rates. OC is among the most prevalent types of cancer that affect the head and neck region, and the overall survival rate at 5 years is still around 50%. Moreover, it is a multifactorial malignancy instigated by genetic and epigenetic variabilities, and molecular heterogeneity makes it a complex malignancy. Oral potentially malignant disorders (OPMDs) are often the first warning signs of OC, although it is challenging to predict which cases will develop into malignancies. Visual oral examination and histological examination are still the standard initial steps in diagnosing oral lesions; however, these approaches have limitations that might lead to late diagnosis of OC or missed diagnosis of OPMDs in high-risk individuals. The objective of this review is to present a comprehensive overview of the currently used novel techniques viz., liquid biopsy, next-generation sequencing (NGS), microarray, nanotechnology, lab-on-a-chip (LOC) or microfluidics, and artificial intelligence (AI) for the clinical diagnostics and management of this malignancy. The potential of these novel techniques in expanding OC diagnostics and clinical management is also reviewed.

Artificial-Intelligence-Based Decision Making for Oral Potentially Malignant Disorder Diagnosis in Internet of Medical Things Environment

Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models.

Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis

Machine learning (ML) algorithms are becoming increasingly pervasive in the domains of medical diagnostics and prognostication, afforded by complex deep learning architectures that overcome the limitations of manual feature extraction. In this systematic review and meta-analysis, we provide an update on current progress of ML algorithms in point-of-care (POC) automated diagnostic classification systems for lesions of the oral cavity. Studies reporting performance metrics on ML algorithms used in automatic classification of oral regions of interest were identified and screened by 2 independent reviewers from 4 databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. 35 studies were suitable for qualitative synthesis, and 31 for quantitative analysis. Outcomes were assessed using a bivariate random-effects model following an assessment of bias and heterogeneity. 4 distinct methodologies were identified for POC diagnosis: (1) clinical photography; (2) optical imaging; (3) thermal imaging; (4) analysis of volatile organic compounds. Estimated AUROC across all studies was 0.935, and no difference in performance was identified between methodologies. We discuss the various classical and modern approaches to ML employed within identified studies, and highlight issues that will need to be addressed for implementation of automated classification systems in screening and early detection.

Review of the Use of Artificial Intelligence in Early Diagnosis and Prevention of Oral Cancer

The global occurrence of oral cancer (OC) has increased in recent years. OC that is diagnosed in its advanced stages results in morbidity and mortality. The use of technology may be beneficial for early detection and diagnosis and thus help the clinician with better patient management. The advent of artificial intelligence (AI) has the potential to improve OC screening. AI can precisely analyze an enormous dataset from various imaging modalities and provide assistance in the field of oncology. This review focused on the applications of AI in the early diagnosis and prevention of OC. A literature search was conducted in the PubMed and Scopus databases using the search terminology “oral cancer” and “artificial intelligence.” Further information regarding the topic was collected by scrutinizing the reference lists of selected articles. Based on the information obtained, this article reviews and discusses the applications and advantages of AI in OC screening, early diagnosis, disease prediction, treatment planning, and prognosis. Limitations and the future scope of AI in OC research are also highlighted.

The Effectiveness of Artificial Intelligence in Detection of Oral Cancer

Aim

The early detection of oral cancer (OC) at the earliest stage significantly increases survival rates. Recently, there has been an increasing interest in the use of artificial intelligence (AI) technologies in diagnostic medicine. This study aimed to critically analyse the available evidence concerning the utility of AI in the diagnosis of OC. Special consideration was given to the diagnostic accuracy of AI and its ability to identify the early stages of OC.

Materials and methods

From the date of inception to December 2021, 4 databases (PubMed, Scopus, EBSCO, and OVID) were searched. Three independent authors selected studies on the basis of strict inclusion criteria. The risk of bias and applicability were assessed using the prediction model risk of bias assessment tool. Of the 606 initial records, 17 studies with a total of 7245 patients and 69,425 images were included. Ten statistical methods were used to assess AI performance in the included studies. Six studies used supervised machine learning, whilst 11 used deep learning. The results of deep learning ranged with an accuracy of 81% to 99.7%, sensitivity 79% to 98.75%, specificity 82% to 100%, and area under the curve (AUC) 79% to 99.5%.

Results

Results obtained from supervised machine learning demonstrated an accuracy ranging from 43.5% to 100%, sensitivity of 94% to 100%, specificity 16% to 100%, and AUC of 93%.

Conclusions

There is no clear consensus regarding the best AI method for OC detection. AI is a valuable diagnostic tool that represents a large evolutionary leap in the detection of OC in its early stages. Based on the evidence, deep learning, such as a deep convolutional neural network, is more accurate in the early detection of OC compared to supervised machine learning.

An Efficient Memristor-Based Circuit Implementation of Squeeze-and-Excitation Fully Convolutional Neural Networks

Recently, there has been a surge of interest in applying memristors to hardware implementations of deep neural networks due to various desirable properties of the memristor, such as nonvolativity, multivalue, and nanosize. Most existing neural network circuit designs, however, are based on generic frameworks that are not optimized for memristors. Furthermore, to the best of our knowledge, there are no existing efficient memristor-based implementations of complex neural network operators, such as deconvolutions and squeeze-and-excitation (SE) blocks, which are critical for achieving high accuracy in common medical image analysis applications, such as semantic segmentation. This article proposes convolution-kernel first (CKF), an efficient scheme for designing memristor-based fully convolutional neural networks (FCNs). Compared with existing neural network circuits, CKF enables effective parameter pruning, which significantly reduces circuit power consumption. Furthermore, CKF includes the novel, memristor-optimized implementations of deconvolution layers and SE blocks. Simulation results on real medical image segmentation tasks confirm that CKF obtains up to 56.2% reduction in terms of computations and 33.62-W reduction in terms of power consumption in the circuit after weight pruning while retaining high accuracy on the test set. Moreover, the pruning results can be applied directly to existing circuits without any modification for the corresponding system.

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review

The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases-PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.

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.

Fractal Dimension and Texture Analysis of Lesion Autofluorescence in the Evaluation of Oral Lichen Planus Treatment Effectiveness

BACKGROUND

Oral Lichen planus (OLP) is a chronic inflammatory disease. Topical steroids are used as the treatment of choice. The alternative is photodynamic therapy (PDT). The study aimed to fabricate optimal biodegradable matrices for methylene blue or triamcinolone acetonide because of a lack of currently commercially available carriers that could adhere to the mucous.

METHODS

The study was designed as a 12-week single-blind prospective randomized clinical trial with 30 patients, full contralateral split-mouth design. Matrices for steroid and photosensitizer and laser device were fabricated. Fractal and texture analysis of photographs, taken in 405, 450, 405 + 450 nm wavelength, of lesions was performed to increase the objectivity of the assessment of treatment.

RESULTS

We achieved two total responses for treatment in case of steroid therapy and one in the case of PDT. Partial response was noted in 17 lesions treated using local steroid therapy and 21 in the case of PDT. No statistically significant differences were found between the effectiveness of both used methods. Statistically significant differences in fractal dimension before and after treatment were observed only in the analysis of photographs taken in 405 + 450 nm wavelength.

CONCLUSIONS

Photodynamic therapy and topical steroid therapy are effective methods for treating OLP. Using a carrier offers the possibility of a more predictable and effective method of drug delivery into the mucous membrane. Autofluorescence enables the detection of lesions especially at the early stage of their development.

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

The early diagnosis of cancer can facilitate subsequent clinical patient management. Artificial intelligence (AI) has been found to be promising for improving the diagnostic process. The aim of the present study is to increase the evidence on the application of AI to the early diagnosis of oral cancer through a scoping review. A search was performed in the PubMed, Web of Science, Embase and Google Scholar databases during the period from January 2000 to December 2020, referring to the early non-invasive diagnosis of oral cancer based on AI applied to screening. Only accessible full-text articles were considered. Thirty-six studies were included on the early detection of oral cancer based on images (photographs (optical imaging and enhancement technology) and cytology) with the application of AI models. These studies were characterized by their heterogeneous nature. Each publication involved a different algorithm with potential training data bias and few comparative data for AI interpretation. Artificial intelligence may play an important role in precisely predicting the development of oral cancer, though several methodological issues need to be addressed in parallel to the advances in AI techniques, in order to allow large-scale transfer of the latter to population-based detection protocols.

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.

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue

Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI-based system has great potential in the diagnosis of OSCC.

Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review

BACKGROUND

Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care.

OBJECTIVES

This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice.

DATA SOURCES

We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC.

ELIGIBILITY CRITERIA

Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered.

DATA EXTRACTION

Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies.

RESULTS

A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations.

CONCLUSION

Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.