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

Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects

BACKGROUND

Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction.

OBJECTIVES

This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC). It further explores the workflow of AI-based radiomics for personalized and precision oncology in HNC. Finally, it examines the current challenges of AI-based radiomics in daily clinical oncology and offers possible solutions to these challenges.

METHODS

Comprehensive electronic databases (PubMed, Medline via Ovid, Scopus, Web of Science, CINAHL, and Cochrane Library) were searched following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The quality of included studies and their risk of biases were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)and Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

Out of the 659 search hits retrieved, 45 fulfilled the inclusion criteria. Our review revealed that the application of AI-based radiomics model as an ancillary tool for improved decision-making in HNC management includes radiomics-based cancer diagnosis and radiomics-based cancer prognosis. The radiomics-based cancer diagnosis includes tumor staging, tumor grading, and classification of malignant and benign tumors. Similarly, radiomics-based cancer prognosis includes prediction for treatment response, recurrence, metastasis, and survival. In addition, the challenges in the implementation of these models for clinical evaluations include data imbalance, feature engineering (extraction and selection), model generalizability, multi-modal fusion, and model interpretability.

CONCLUSION

Considering the highly subjective and interobserver variability that is peculiar to the interpretation of medical images by expert clinicians, AI-based radiomics seeks to offer potentially useful quantitative information, which is not visible to the human eye or unintentionally often remain ignored during clinical imaging practice. By enabling the extraction of this type of information, AI-based radiomics has the potential to revolutionize HNC oncology, providing a platform for more personalized, higher quality, and cost-effective care for HNC patients.

Population-Based Prognostic Models for Head and Neck Cancers Using National Cancer Registry Data from Taiwan

PURPOSE

This study aims to raise awareness of the disparities in survival predictions among races in head and neck cancer (HNC) patients by developing and validating population-based prognostic models specifically tailored for Taiwanese and Asian populations.

METHODS

A total of 49,137 patients diagnosed with HNCs were included from the Taiwan Cancer Registry (TCR). Six prognostic models, divided into three categories based on surgical status, were developed to predict both overall survival (OS) and cancer-specific survival using the registered demographic and clinicopathological characteristics in the Cox proportional hazards model. The prognostic models underwent internal evaluation through a tenfold cross-validation among the TCR Taiwanese datasets and external validation across three primary racial populations using the Surveillance, Epidemiology, and End Results database. Predictive performance was assessed using discrimination analysis employing Harrell's c-index and calibration analysis with proportion tests.

RESULTS

The TCR training and testing datasets demonstrated stable and favorable predictive performance, with all Harrell's c-index values ≥ 0.7 and almost all differences in proportion between the predicted and observed mortality being < 5%. In external validation, Asians exhibited the best performance compared with white and black populations, particularly in predicting OS, with all Harrell's c-index values > 0.7.

CONCLUSIONS

Survival predictive disparities exist among different racial groups in HNCs. We have developed population-based prognostic models for Asians that can enhance clinical practice and treatment plans.

Comparison of Flaps From Different Sites for Repairing Postoperative Soft Tissue Defects Regarding the Quality of Life in Patients With Oral Cancer: A Systematic Review

This study aims to compare flaps at different sites in treating soft tissue defects after oral cancer surgery and improving patients' quality of life (QoL). Databases were searched until September 2023. The extracted data included the scores of chewing, swallowing, speech, mood, and appearance based on the University of Washington QoL questionnaire, version 4. Two types of free flaps and 2 types of pedicled tissue flaps were included. The free flaps were the forearm free flap (FFF) and anterolateral thigh flap, and the pedicled tissue flaps were the submental artery island flap and pectoralis major myocutaneous flap (PMMF). Compared with FFF, there was no significant difference in the scores of chewing, swallowing, speech, and mood among anterolateral thigh, submental artery island flap, and PMMF, and PMMF generally had a higher score than FFF only in terms of appearance, with statistical significance. There is no significant difference in chewing, swallowing, speech, and mood between flaps from different sites in repairing postoperative soft tissue defects of oral cancer. Therefore, the widely used FFF may be the preferred choice considering the QoL of patients after oral cancer surgery.

Survival estimation of oral cancer using fuzzy deep learning

BACKGROUND

Oral cancer is a deadly disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop a fuzzy deep learning (FDL)-based model to estimate the survival time based on clinicopathologic data of oral cancer.

METHODS

Electronic medical records of 581 oral squamous cell carcinoma (OSCC) patients, treated with surgery with or without radiochemotherapy, were collected retrospectively from the Oral and Maxillofacial Surgery Clinic and the Regional Cancer Center from 2011 to 2019. The deep learning (DL) model was trained to classify survival time classes based on clinicopathologic data. Fuzzy logic was integrated into the DL model and trained to create FDL-based models to estimate the survival time classes.

RESULTS

The performance of the models was evaluated on a test dataset. The performance of the DL and FDL models for estimation of survival time achieved an accuracy of 0.74 and 0.97 and an area under the receiver operating characteristic (AUC) curve of 0.84 to 1.00 and 1.00, respectively.

CONCLUSIONS

The integration of fuzzy logic into DL models could improve the accuracy to estimate survival time based on clinicopathologic data of oral cancer.

Evaluation of bone marrow invasion on the machine learning of 18 F-FDG PET texture analysis in lower gingival squamous cell carcinoma

OBJECTIVES

Lower gingival squamous cell carcinoma (LGSCC) has the potential to invade the alveolar bone. Traditionally, the diagnosis of LGSCC relied on morphological imaging, but inconsistencies between these assessments and surgical findings have been observed. This study aimed to assess the correlation between LGSCC bone marrow invasion and PET texture features and to enhance diagnostic accuracy by using machine learning.

METHODS

A retrospective analysis of 159 LGSCC patients with pretreatment 18 F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) examination from 2009 to 2017 was performed. We extracted radiomic features from the PET images, focusing on pathologic bone marrow invasion detection. Extracted features underwent the least absolute shrinkage and selection operator algorithm-based selection and were then used for machine learning via the XGBoost package to distinguish bone marrow invasion presence. Receiver operating characteristic curve analysis was performed.

RESULTS

From the 159 patients, 88 qualified for further analysis (59 men; average age, 69.2 years), and pathologic bone marrow invasion was identified in 69 (78%) of these patients. Three significant radiological features were identified: Gray level co-occurrence matrix_Correlation, INTENSITY-BASED_IntensityInterquartileRange, and MORPHOLOGICAL_SurfaceToVolumeRatio. An XGBoost machine-learning model, using PET radiomic features to detect bone marrow invasion, yielded an area under the curve value of 0.83.

CONCLUSION

Our findings highlighted the potential of 18 F-FDG PET radiomic features, combined with machine learning, as a promising avenue for improving LGSCC diagnosis and treatment. Using 18 F-FDG PET texture features may provide a robust and accurate method for determining the presence or absence of bone marrow invasion in LGSCC patients.

Geriatrics and artificial intelligence in Spain (Ger-IA project): talking to ChatGPT, a nationwide survey

PURPOSE

The purposes of the study was to describe the degree of agreement between geriatricians with the answers given by an AI tool (ChatGPT) in response to questions related to different areas in geriatrics, to study the differences between specialists and residents in geriatrics in terms of the degree of agreement with ChatGPT, and to analyse the mean scores obtained by areas of knowledge/domains.

METHODS

An observational study was conducted involving 126 doctors from 41 geriatric medicine departments in Spain. Ten questions about geriatric medicine were posed to ChatGPT, and doctors evaluated the AI's answers using a Likert scale. Sociodemographic variables were included. Questions were categorized into five knowledge domains, and means and standard deviations were calculated for each.

RESULTS

130 doctors answered the questionnaire. 126 doctors (69.8% women, mean age 41.4 [9.8]) were included in the final analysis. The mean score obtained by ChatGPT was 3.1/5 [0.67]. Specialists rated ChatGPT lower than residents (3.0/5 vs. 3.3/5 points, respectively, P < 0.05). By domains, ChatGPT ​​scored better (M: 3.96; SD: 0.71) in general/theoretical questions rather than in complex decisions/end-of-life situations (M: 2.50; SD: 0.76) and answers related to diagnosis/performing of complementary tests obtained the lowest ones (M: 2.48; SD: 0.77).

CONCLUSION

Scores presented big variability depending on the area of knowledge. Questions related to theoretical aspects of challenges/future in geriatrics obtained better scores. When it comes to complex decision-making, appropriateness of the therapeutic efforts or decisions about diagnostic tests, professionals indicated a poorer performance. AI is likely to be incorporated into some areas of medicine, but it would still present important limitations, mainly in complex medical decision-making.

Applying Machine Learning for Enhanced MicroRNA Analysis: A Companion Risk Tool for Oral Squamous Cell Carcinoma in Standard Care Incisional Biopsy

Machine learning analyses within the realm of oral cancer outcomes are relatively underexplored compared to other cancer types. This study aimed to assess the performance of machine learning algorithms in identifying oral cancer patients, utilizing microRNA expression data. In this study, we implemented this approach using a panel of oral cancer-associated microRNAs sourced from standard incisional biopsy specimens to identify cases of oral squamous cell carcinomas (OSCC). For the model development process, we used a dataset comprising 30 OSCC and 30 histologically normal epithelium (HNE) cases. We initially trained a logistic regression prediction model using 70 percent of the dataset, while reserving the remaining 30 percent for testing. Subsequently, the model underwent hyperparameter tuning resulting in enhanced performance metrics. The hyperparameter-tuned model exhibited high accuracy (0.894) and ROC AUC (0.898) in predicting OSCC. Testing the model on cases of potentially malignant disorders (OPMDs) revealed that leukoplakia with mild dysplasia was predicted as having a high risk of progressing to OSCC, emphasizing machine learning's advantage over histopathology in detecting early molecular changes. These findings underscore the necessity for further refinement, incorporating a broader set of variables to enhance the model's predictive capabilities in assessing the risk of oral potentially malignant disorders.

Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient

PURPOSE OF REVIEW

The purpose of this review is to present recent advances and limitations in machine learning applied to the evaluation of speech, voice, and swallowing in head and neck cancer.

RECENT FINDINGS

Novel machine learning models incorporating diverse data modalities with improved discriminatory capabilities have been developed for predicting toxicities following head and neck cancer therapy, including dysphagia, dysphonia, xerostomia, and weight loss as well as guiding treatment planning. Machine learning has been applied to the care of posttreatment voice and swallowing dysfunction by offering objective and standardized assessments and aiding innovative technologies for functional restoration. Voice and speech are also being utilized in machine learning algorithms to screen laryngeal cancer.

SUMMARY

Machine learning has the potential to help optimize, assess, predict, and rehabilitate voice and swallowing function in head and neck cancer patients as well as aid in cancer screening. However, existing studies are limited by the lack of sufficient external validation and generalizability, insufficient transparency and reproducibility, and no clear superior predictive modeling strategies. Algorithms and applications will need to be trained on large multiinstitutional data sets, incorporate sociodemographic data to reduce bias, and achieve validation through clinical trials for optimal performance and utility.

Ginsenoside Rb1 Deters Cell Proliferation, Induces Apoptosis, Alleviates Oxidative Stress, and Antimetastasis in Oral Squamous Carcinoma Cells

There are numerous therapeutic applications for ginsenoside Rb1 (GRb1), the primary saponin derived from ginseng root. According to earlier research, ginsenoside Rb1 causes apoptosis and reduces the cell cycle. Its adverse effects, especially those on the development of the embryo, still need to be thoroughly studied. A host's lifestyle choices, including smoking, drinking too much alcohol, using tobacco products, and having an HPV infection, can increase the risk of oral squamous cell carcinoma (OSCC), one of the most prevalent malignancies of the oral cavity. To address this challenge, this investigation focuses on the design of GRb1 for treating OSCC. In vitro cytotoxicity studies confirmed that GRb1 was more effective in PCI-9A and PCI-13 cells, with reduced toxicity in non-cancerous cells. Further verification of cellular morphology was achieved through various biochemical staining methods. The mechanism of cell death was investigated by Annexin V-FITC and PI methods. Additionally, the antimetastatic attributes of GRb1 have been evaluated using both migration scratch and Transwell migration assays, which have collectively revealed excellent antimetastatic potential. The DNA fragmentation of the PCI-9A and PCI-13 cells was assessed using a comet assay. Ginsenoside Rb1 improved ROS levels and caused mitochondrial membrane potential alterations and DNA damage, which resulted in apoptosis. OSCC administration significantly reduced the levels of SOD, GSH, GPx, and CAT, increasing the levels of PCI-9A and PCI-13 cells, while GRb1 improved this situation. Therefore, we propose that Ginsenoside Rb1 could be an alternative therapeutic strategy for OSCC therapy.

Multimodal artificial intelligence-based pathogenomics improves survival prediction in oral squamous cell carcinoma

In this study, we aimed to develop a novel prognostic algorithm for oral squamous cell carcinoma (OSCC) using a combination of pathogenomics and AI-based techniques. We collected comprehensive clinical, genomic, and pathology data from a cohort of OSCC patients in the TCGA dataset and used machine learning and deep learning algorithms to identify relevant features that are predictive of survival outcomes. Our analyses included 406 OSCC patients. Initial analyses involved gene expression analyses, principal component analyses, gene enrichment analyses, and feature importance analyses. These insights were foundational for subsequent model development. Furthermore, we applied five machine learning/deep learning algorithms (Random Survival Forest, Gradient Boosting Survival Analysis, Cox PH, Fast Survival SVM, and DeepSurv) for survival prediction. Our initial analyses revealed relevant gene expression variations and biological pathways, laying the groundwork for robust feature selection in model building. The results showed that the multimodal model outperformed the unimodal models across all methods, with c-index values of 0.722 for RSF, 0.633 for GBSA, 0.625 for FastSVM, 0.633 for CoxPH, and 0.515 for DeepSurv. When considering only important features, the multimodal model continued to outperform the unimodal models, with c-index values of 0.834 for RSF, 0.747 for GBSA, 0.718 for FastSVM, 0.742 for CoxPH, and 0.635 for DeepSurv. Our results demonstrate the potential of pathogenomics and AI-based techniques in improving the accuracy of prognostic prediction in OSCC, which may ultimately aid in the development of personalized treatment strategies for patients with this devastating disease.

Designing explainable AI to improve human-AI team performance: A medical stakeholder-driven scoping review

The rise of complex AI systems in healthcare and other sectors has led to a growing area of research called Explainable AI (XAI) designed to increase transparency. In this area, quantitative and qualitative studies focus on improving user trust and task performance by providing system- and prediction-level XAI features. We analyze stakeholder engagement events (interviews and workshops) on the use of AI for kidney transplantation. From this we identify themes which we use to frame a scoping literature review on current XAI features. The stakeholder engagement process lasted over nine months covering three stakeholder group's workflows, determining where AI could intervene and assessing a mock XAI decision support system. Based on the stakeholder engagement, we identify four major themes relevant to designing XAI systems - 1) use of AI predictions, 2) information included in AI predictions, 3) personalization of AI predictions for individual differences, and 4) customizing AI predictions for specific cases. Using these themes, our scoping literature review finds that providing AI predictions before, during, or after decision-making could be beneficial depending on the complexity of the stakeholder's task. Additionally, expert stakeholders like surgeons prefer minimal to no XAI features, AI prediction, and uncertainty estimates for easy use cases. However, almost all stakeholders prefer to have optional XAI features to review when needed, especially in hard-to-predict cases. The literature also suggests that providing both system- and prediction-level information is necessary to build the user's mental model of the system appropriately. Although XAI features improve users' trust in the system, human-AI team performance is not always enhanced. Overall, stakeholders prefer to have agency over the XAI interface to control the level of information based on their needs and task complexity. We conclude with suggestions for future research, especially on customizing XAI features based on preferences and tasks.

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.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

The Effectiveness of an Adaptive Method to Analyse the Transition between Tumour and Peritumour for Answering Two Clinical Questions in Cancer Imaging

Based on the well-known role of peritumour characterization in cancer imaging to improve the early diagnosis and timeliness of clinical decisions, this study innovated a state-of-the-art approach for peritumour analysis, mainly relying on extending tumour segmentation by a predefined fixed size. We present a novel, adaptive method to investigate the zone of transition, bestriding tumour and peritumour, thought of as an annular-like shaped area, and detected by analysing gradient variations along tumour edges. For method validation, we applied it on two datasets (hepatocellular carcinoma and locally advanced rectal cancer) imaged by different modalities and exploited the zone of transition regions as well as the peritumour ones derived by adopting the literature approach for building predictive models. To measure the zone of transition's benefits, we compared the predictivity of models relying on both "standard" and novel peritumour regions. The main comparison metrics were informedness, specificity and sensitivity. As regards hepatocellular carcinoma, having circular and regular shape, all models showed similar performance (informedness = 0.69, sensitivity = 84%, specificity = 85%). As regards locally advanced rectal cancer, with jagged contours, the zone of transition led to the best informedness of 0.68 (sensitivity = 89%, specificity = 79%). The zone of transition advantages include detecting the peritumour adaptively, even when not visually noticeable, and minimizing the risk (higher in the literature approach) of including adjacent diverse structures, which was clearly highlighted during image gradient analysis.

Interpretable machine learning model for prediction of overall survival in laryngeal cancer

Background: The mortality rates of laryngeal squamous cell carcinoma cancer (LSCC) have not significantly decreased in the last decades.Objectives: We primarily aimed to compare the predictive performance of DeepTables with the state-of-the-art machine learning (ML) algorithms (Voting ensemble, Stack ensemble, and XGBoost) to stratify patients with LSCC into chance of overall survival (OS). In addition, we complemented the developed model by providing interpretability using both global and local model-agnostic techniques.Methods: A total of 2792 patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with LSCC were reviewed. The global model-agnostic interpretability was examined using SHapley Additive exPlanations (SHAP) technique. Likewise, individual interpretation of the prediction was made using Local Interpretable Model Agnostic Explanations (LIME).Results: The state-of-the-art ML ensemble algorithms outperformed DeepTables. Specifically, the examined ensemble algorithms showed comparable weighted area under receiving curve of 76.9, 76.8, and 76.1 with an accuracy of 71.2%, 70.2%, and 71.8%, respectively. The global methods of interpretability (SHAP) demonstrated that the age of the patient at diagnosis, N-stage, T-stage, tumor grade, and marital status are among the prominent parameters.Conclusions: A ML model for OS prediction may serve as an ancillary tool for treatment planning of LSCC patients.

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.

Prediction Model for Unfavorable Outcome in Spontaneous Intracerebral Hemorrhage Based on Machine Learning

OBJECTIVE

The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML).

METHODS

Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR).

RESULTS

We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables.

CONCLUSION

The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.

Systematic reviews of machine learning in healthcare: a literature review

INTRODUCTION

The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery.

METHODS

A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted.

RESULTS

In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively).

EXPERT OPINION

The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.

A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma

OBJECTIVES

In this study, we constructed and validated models based on deep learning and radiomics to facilitate preoperative diagnosis of cervical lymph node metastasis (LNM) using contrast-enhanced computed tomography (CECT).

MATERIALS AND METHODS

CECT scans of 100 patients with OSCC (217 metastatic and 1973 non-metastatic cervical lymph nodes: development set, 76 patients; internally independent test set, 24 patients) who received treatment at the Peking University School and Hospital of Stomatology between 2012 and 2016 were retrospectively collected. Clinical diagnoses and pathological findings were used to establish the gold standard for metastatic cervical LNs. A reader study with two clinicians was also performed to evaluate the lymph node status in the test set. The performance of the proposed models and the clinicians was evaluated and compared by measuring using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE).

RESULTS

A fusion model combining deep learning with radiomics showed the best performance (ACC, 89.2%; SEN, 92.0%; SPE, 88.9%; and AUC, 0.950 [95% confidence interval: 0.908-0.993, P < 0.001]) in the test set. In comparison with the clinicians, the fusion model showed higher sensitivity (92.0 vs. 72.0% and 60.0%) but lower specificity (88.9 vs. 97.5% and 98.8%).

CONCLUSION

A fusion model combining radiomics and deep learning approaches outperformed other single-technique models and showed great potential to accurately predict cervical LNM in patients with OSCC.

CLINICAL RELEVANCE

The fusion model can complement the preoperative identification of LNM of OSCC performed by the clinicians.

Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas

BACKGROUND

Odontogenic tumors (OT) are composed of heterogeneous lesions, which can be benign or malignant, with different behavior and histology. Within this classification, ameloblastoma and ameloblastic carcinoma (AC) represent a diagnostic challenge in daily histopathological practice due to their similar characteristics and the limitations that incisional biopsies represent. From these premises, we wanted to test the usefulness of models based on artificial intelligence (AI) in the field of oral and maxillofacial pathology for differential diagnosis. The main advantages of integrating Machine Learning (ML) with microscopic and radiographic imaging is the ability to significantly reduce intra-and inter observer variability and improve diagnostic objectivity and reproducibility.

METHODS

Thirty Digitized slides were collected from different diagnostic centers of oral pathology in Brazil. After performing manual annotation in the region of interest, the images were segmented and fragmented into small patches. In the supervised learning methodology for image classification, three models (ResNet50, DenseNet, and VGG16) were focus of investigation to provide the probability of an image being classified as class0 (i.e., ameloblastoma) or class1 (i.e., Ameloblastic carcinoma).

RESULTS

The training and validation metrics did not show convergence, characterizing overfitting. However, the test results were satisfactory, with an average for ResNet50 of 0.75, 0.71, 0.84, 0.65, and 0.77 for accuracy, precision, sensitivity, specificity, and F1-score, respectively.

CONCLUSIONS

The models demonstrated a strong potential of learning, but lack of generalization ability. The models learn fast, reaching a training accuracy of 98%. The evaluation process showed instability in validation; however, acceptable performance in the testing process, which may be due to the small data set. This first investigation opens an opportunity for expanding collaboration to incorporate more complementary data; as well as, developing and evaluating new alternative models.

A comprehensive prognostic score for head and neck squamous cancer driver genes and phenotype traits

BACKGROUND

Head and neck squamous cancer (HNSCC) presents variable phenotype and progression features. Clinically applicable, high-accuracy multifactorial prognostic models for HNSCC survival outcomes are warranted and an active area of research. This study aimed to construct a comprehensive prognostic tool for HNSCC overall survival by integrating cancer driver genes with tumor clinical and phenotype information.

METHODS

Key overall survival-related cancer driver genes were screened from among main effector and reciprocal gene pairs using TCGA data using univariate Cox proportional hazard regression analysis. Independent validation was performed using the GSE41613 dataset. The main effector genes among these were selected using LASSO regression and transcriptome score modeling was performed using multivariate Cox regression followed by validation analysis of the prognostic score. Next, multivariate Cox regression analysis was performed using the transcriptome score combined with age, grade, gender, and stage. An 'Accurate Prediction Model of HNSCC Overall Survival Score' (APMHO) was computed and validated. Enriched functional pathways, gene mutational landscape, immune cell infiltration, and immunotherapy sensitivity markers associated with high and low APMHO scores were analyzed.

RESULTS

Screening 107 overall survival-related cancer genes and 402 interacting gene pairs, 6 genes: CRLF2, HSP90AA1, MAP2K1, PAFAH1B2, MYCL and SET genes, were identified and a transcriptional score was obtained. Age, stage and transcriptional score were found to be significant predictors in Cox regression analysis and used to construct a final APMHO model showing an AUC > 0.65 and validated. Transcriptional score, age, pathologic_N, pathologic_T, stage, and TCGA_subtype were significantly different in distribution between high and low APMHO groups. High APMHO samples showed significantly higher mutation rate, enriched tumor-related pathways including Hypoxia, unfold_protein_response, Glycolysis, and mTORC1 signaling, along with differences in immune cell infiltration and immune checkpoint, interferon-γ pathway and m6A regulator expression patterns.

CONCLUSION

The APMHO score combining transcriptional and clinical variables showed good prognostic ability for HNSCC overall survival outcomes and was associated with different patterns of phenotypical features, immune and mutational landscape, and immunotherapy sensitivity marker expression. Future studies should validate this score in independent clinical cohorts.

Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs

The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification of oral cancer using microscopic histopathology slide images. This innovative system capitalizes on the power of convolutional neural networks (CNNs), strengthened by the synergy of transfer learning (TL), and further fine-tuned using the novel Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO), two cutting-edge metaheuristic optimization algorithms. This integration is a novel approach, addressing bias and unpredictability issues commonly encountered in the preprocessing and optimization phases. In the experiments, the capabilities of well-established pre-trained TL models, including VGG19, VGG16, MobileNet, MobileNetV3Small, MobileNetV2, MobileNetV3Large, NASNetMobile, and DenseNet201, all initialized with 'ImageNet' weights, were harnessed. The experimental dataset consisted of the Histopathologic Oral Cancer Detection dataset, which includes a 'normal' class with 2494 images and an 'OSCC' (oral squamous cell carcinoma) class with 2698 images. The results reveal a remarkable performance distinction between the AO and GTO, with the AO consistently outperforming the GTO across all models except for the Xception model. The DenseNet201 model stands out as the most accurate, achieving an astounding average accuracy rate of 99.25% with the AO and 97.27% with the GTO. This innovative framework signifies a significant leap forward in automating oral cancer detection, showcasing the tremendous potential of applying optimized deep learning models in the realm of healthcare diagnostics. The integration of the AO and GTO in our CNN-based system not only pushes the boundaries of classification accuracy but also underscores the transformative impact of metaheuristic optimization techniques in the field of medical image analysis.

An Intelligent System to Improve Diagnostic Support for Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most-prevalent cancer types worldwide, and it poses a serious threat to public health due to its high mortality and morbidity rates. OSCC typically has a poor prognosis, significantly reducing the chances of patient survival. Therefore, early detection is crucial to achieving a favorable prognosis by providing prompt treatment and increasing the chances of remission. Salivary biomarkers have been established in numerous studies to be a trustworthy and non-invasive alternative for early cancer detection. In this sense, we propose an intelligent system that utilizes feed-forward artificial neural networks to classify carcinoma with salivary biomarkers extracted from control and OSCC patient samples. We conducted experiments using various salivary biomarkers, ranging from 1 to 51, to train the model, and we achieved excellent results with precision, sensitivity, and specificity values of 98.53%, 96.30%, and 97.56%, respectively. Our system effectively classified the initial cases of OSCC with different amounts of biomarkers, aiding medical professionals in decision-making and providing a more-accurate diagnosis. This could contribute to a higher chance of treatment success and patient survival. Furthermore, the minimalist configuration of our model presents the potential for incorporation into resource-limited devices or environments.

Oral squamous cell carcinomas: state of the field and emerging directions

Oral squamous cell carcinoma (OSCC) develops on the mucosal epithelium of the oral cavity. It accounts for approximately 90% of oral malignancies and impairs appearance, pronunciation, swallowing, and flavor perception. In 2020, 377,713 OSCC cases were reported globally. According to the Global Cancer Observatory (GCO), the incidence of OSCC will rise by approximately 40% by 2040, accompanied by a growth in mortality. Persistent exposure to various risk factors, including tobacco, alcohol, betel quid (BQ), and human papillomavirus (HPV), will lead to the development of oral potentially malignant disorders (OPMDs), which are oral mucosal lesions with an increased risk of developing into OSCC. Complex and multifactorial, the oncogenesis process involves genetic alteration, epigenetic modification, and a dysregulated tumor microenvironment. Although various therapeutic interventions, such as chemotherapy, radiation, immunotherapy, and nanomedicine, have been proposed to prevent or treat OSCC and OPMDs, understanding the mechanism of malignancies will facilitate the identification of therapeutic and prognostic factors, thereby improving the efficacy of treatment for OSCC patients. This review summarizes the mechanisms involved in OSCC. Moreover, the current therapeutic interventions and prognostic methods for OSCC and OPMDs are discussed to facilitate comprehension and provide several prospective outlooks for the fields.

Whole-tumor histogram analysis of apparent diffusion coefficient maps with machine learning algorithms for predicting histologic grade of sinonasal squamous cell carcinoma: a preliminary study

PURPOSE

Accurate histologic grade assessment is helpful for clinical decision making and prognostic assessment of sinonasal squamous cell carcinoma (SNSCC). This research aimed to explore whether whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps with machine learning algorithms can predict histologic grade of SNSCC.

METHODS

One hundred and forty-seven patients with pathologically diagnosed SNSCC formed this retrospective study. Sixty-six patients were low-grade (grade I/II) and eighty-one patients were high-grade (grade III). Eighteen histogram features were obtained from quantitative ADC maps. Additionally, the mean ADC value and clinical features were analyzed for comparison with histogram features. Machine learning algorithms were applied to build the best diagnostic model for predicting histological grade. The receiver operating characteristic (ROC) curve was used to evaluate the performance of each model prediction, and the area under the ROC curve (AUC) were analyzed.

RESULTS

The histogram model based on three features (10th Percentile, Mean, and 90th Percentile) with support vector machine (SVM) classifier demonstrated excellent diagnostic performance, with an AUC of 0.947 on the testing dataset. The AUC of the histogram model was similar to that of the mean ADC value model (0.947 vs 0.957; P = 0.7029). The poor diagnostic performance of the clinical model (AUC = 0.692) was improved by the combined model incorporating histogram features or mean ADC value (P < 0.05).

CONCLUSION

ADC histogram analysis improved the projection of SNSCC histologic grade, compared with clinical model. The complex histogram model had comparable but not better performance than mean ADC value model.

An idiosyncratic MIMBO-NBRF based automated system for child birth mode prediction

Predicting the mode of child birth is still remains one of the most complex and challenging tasks in ancient times. Also, there is no such strong methodologies are developed in the conventional works for birth mode prediction. Therefore, the proposed work objects to develop a novel and distinct optimization based machine learning technique for creating the child birth mode prediction system. This framework includes the modules of data imputation, feature selection, classification, and prediction. Initially, the data imputation process is performed to improve the quality of dataset by normalizing the attributes and filling the missed fields. Then, the Multivariate Intensified Mine Blast Optimization (MIMBO) technique is implemented to choose the best set of features by estimating the optimal function. After that, an integrated Naïve Bayes - Random Forest (NBRF) technique is developed by incorporating the functions of conventional NB and RF techniques. The novel contribution of this technique, a Bird Mating (BM) optimization technique is used in NBRF classifier for estimating the likelihood parameter to generate the Bayesian rules. The main idea of this paper is to develop a simple as well as efficient automated system with the use of hybrid machine learning model for predicting the mode of child birth. For this purpose, advanced algorithms such as MIMBO based feature selection, and NBRF based classification are implemented in this work. Due to the inclusion of MIMBO and BM optimization techniques, the performance of classifier is greatly improved with low computational burden and increased prediction accuracy. Moreover, the combination of proposed MIMBO-NBRF technique outperforms the existing child birth prediction methods with superior results in terms of average accuracy up to 99 %. In addition, some other parameters are also estimated and compared with the existing techniques for proving the overall superiority of the proposed framework.

Artificial intelligence ethics in precision oncology: balancing advancements in technology with patient privacy and autonomy

Precision oncology is a rapidly evolving field that uses advanced technologies to deliver personalized cancer care based on a patient's unique genetic and clinical profile. The use of artificial intelligence (AI) in precision oncology has shown great potential to improve diagnosis, treatment planning, and treatment outcomes. However, the integration of AI in precision oncology also raises important ethical considerations related to patient privacy, autonomy, and protection from bias. In this opinion paper, an overview is provided of previous studies that have explored the use of AI in precision oncology and the ethical considerations associated with this technology. The conclusions of these studies are compared, and the importance of approaching the use of AI in precision oncology with caution is emphasized. It is stressed that patient privacy, autonomy, and protection from bias should be made central to the development and use of AI in precision oncology. Clear guidelines and regulations must be established to ensure that AI is used ethically and for the benefit of patients. The use of AI in precision oncology has the potential to revolutionize cancer care, but it should be ensured that it striked a balance between advancements in technology and ethical considerations. In conclusion, the use of AI in precision oncology is a promising development that has the potential to improve cancer outcomes. However, ethical considerations related to patient privacy, autonomy, and protection from bias must be central to the development and use of AI in precision oncology.

Magnetic resonance imaging-based prediction models for tumor stage and cervical lymph node metastasis of tongue squamous cell carcinoma

Purpose

To evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC).

Materials and methods

108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension-together with shape-based and intensity-based features-were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared.

Results

MRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78-0.92) and 0.81 (0.64-0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57-0.78) and 0.69 (0.51-0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy.

Conclusion

MRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis.

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

INTRODUCTION

Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to optimize patient care in oncology. As a result, clinicians and decision-makers are faced with a plethora of reviews regarding the state of the art of applications of AI for head and neck cancer (HNC) management. This article provides an analysis of systematic reviews on the current status, and of the limitations of the application of AI/ML as adjunctive decision-making tools in HNC management.

METHODS

Electronic databases (PubMed, Medline via Ovid, Scopus, and Web of Science) were searched from inception until November 30, 2022. The study selection, searching and screening processes, inclusion, and exclusion criteria followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A risk of bias assessment was conducted using a tailored and modified version of the Assessment of Systematic Review (AMSTAR-2) tool and quality assessment using the Risk of Bias in Systematic Reviews (ROBIS) guidelines.

RESULTS

Of the 137 search hits retrieved, 17 fulfilled the inclusion criteria. This analysis of systematic reviews revealed that the application of AI/ML as a decision aid in HNC management can be thematized as follows: (1) detection of precancerous and cancerous lesions within histopathologic slides; (2) prediction of the histopathologic nature of a given lesion from various sources of medical imaging; (3) prognostication; (4) extraction of pathological findings from imaging; and (5) different applications in radiation oncology. In addition, the challenges in implementation of AI/ML models for clinical evaluations include the lack of standardized methodological guidelines for the collection of clinical images, development of these models, reporting of their performance, external validation procedures, and regulatory frameworks.

CONCLUSION

At present, there is a paucity of evidence to suggest the adoption of these models in clinical practice due to the aforementioned limitations. Therefore, this manuscript highlights the need for development of standardized guidelines to facilitate the adoption and implementation of these models in the daily clinical practice. In addition, adequately powered, prospective, randomized controlled trials are urgently needed to further assess the potential of AI/ML models in real-world clinical settings for the management of HNC.

A survival prediction model via interpretable machine learning for patients with oropharyngeal cancer following radiotherapy

PURPOSE

To explore interpretable machine learning (ML) methods, with the hope of adding more prognosis value, for predicting survival for patients with Oropharyngeal-Cancer (OPC).

METHODS

A cohort of 427 OPC patients (Training 341, Test 86) from TCIA database was analyzed. Radiomic features of gross-tumor-volume (GTV) extracted from planning CT using Pyradiomics, and HPV p16 status, etc. patient characteristics were considered as potential predictors. A multi-level dimension reduction algorithm consisting of Least-Absolute-Selection-Operator (Lasso) and Sequential-Floating-Backward-Selection (SFBS) was proposed to effectively remove redundant/irrelevant features. The interpretable model was constructed by quantifying the contribution of each feature to the Extreme-Gradient-Boosting (XGBoost) decision by Shapley-Additive-exPlanations (SHAP) algorithm.

RESULTS

The Lasso-SFBS algorithm proposed in this study finally selected 14 features, and our prediction model achieved an area-under-ROC-curve (AUC) of 0.85 on the test dataset based on this feature set. The ranking of the contribution values calculated by SHAP shows that the top predictors that were most correlated with survival were ECOG performance status, wavelet-LLH_firstorder_Mean, chemotherapy, wavelet-LHL_glcm_InverseVariance, tumor size. Those patients who had chemotherapy, with positive HPV p16 status, and lower ECOG performance status, tended to have higher SHAP scores and longer survival; who had an older age at diagnosis, heavy drinking and smoking pack year history, tended to lower SHAP scores and shorter survival.

CONCLUSION

We demonstrated predictive values of combined patient characteristics and imaging features for the overall survival of OPC patients. The multi-level dimension reduction algorithm can reliably identify the most plausible predictors that are mostly associated with overall survival. The interpretable patient-specific survival prediction model, capturing correlations of each predictor and clinical outcome, was developed to facilitate clinical decision-making for personalized treatment.

ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.

Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review

Head and neck tumor differential diagnosis and prognosis have always been a challenge for oral pathologists due to their similarities and complexity. Artificial intelligence novel applications can function as an auxiliary tool for the objective interpretation of histomorphological digital slides. In this review, we present digital histopathological image analysis applications in oral squamous cell carcinoma. A literature search was performed in PubMed MEDLINE with the following keywords: "artificial intelligence" OR "deep learning" OR "machine learning" AND "oral squamous cell carcinoma". Artificial intelligence has proven to be a helpful tool in histopathological image analysis of tumors and other lesions, even though it is necessary to continue researching in this area, mainly for clinical validation.

The Role of Artificial Intelligence Model Documentation in Translational Science: Scoping Review

BACKGROUND

Despite the touted potential of artificial intelligence (AI) and machine learning (ML) to revolutionize health care, clinical decision support tools, herein referred to as medical modeling software (MMS), have yet to realize the anticipated benefits. One proposed obstacle is the acknowledged gaps in AI translation. These gaps stem partly from the fragmentation of processes and resources to support MMS transparent documentation. Consequently, the absence of transparent reporting hinders the provision of evidence to support the implementation of MMS in clinical practice, thereby serving as a substantial barrier to the successful translation of software from research settings to clinical practice.

OBJECTIVE

This study aimed to scope the current landscape of AI- and ML-based MMS documentation practices and elucidate the function of documentation in facilitating the translation of ethical and explainable MMS into clinical workflows.

METHODS

A scoping review was conducted in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. PubMed was searched using Medical Subject Headings key concepts of AI, ML, ethical considerations, and explainability to identify publications detailing AI- and ML-based MMS documentation, in addition to snowball sampling of selected reference lists. To include the possibility of implicit documentation practices not explicitly labeled as such, we did not use documentation as a key concept but as an inclusion criterion. A 2-stage screening process (title and abstract screening and full-text review) was conducted by 1 author. A data extraction template was used to record publication-related information; barriers to developing ethical and explainable MMS; available standards, regulations, frameworks, or governance strategies related to documentation; and recommendations for documentation for papers that met the inclusion criteria.

RESULTS

Of the 115 papers retrieved, 21 (18.3%) papers met the requirements for inclusion. Ethics and explainability were investigated in the context of AI- and ML-based MMS documentation and translation. Data detailing the current state and challenges and recommendations for future studies were synthesized. Notable themes defining the current state and challenges that required thorough review included bias, accountability, governance, and explainability. Recommendations identified in the literature to address present barriers call for a proactive evaluation of MMS, multidisciplinary collaboration, adherence to investigation and validation protocols, transparency and traceability requirements, and guiding standards and frameworks that enhance documentation efforts and support the translation of AI- and ML-based MMS.

CONCLUSIONS

Resolving barriers to translation is critical for MMS to deliver on expectations, including those barriers identified in this scoping review related to bias, accountability, governance, and explainability. Our findings suggest that transparent strategic documentation, aligning translational science and regulatory science, will support the translation of MMS by coordinating communication and reporting and reducing translational barriers, thereby furthering the adoption of MMS.

OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection

Humankind is witnessing a gradual increase in cancer incidence, emphasizing the importance of early diagnosis and treatment, and follow-up clinical protocols. Oral or mouth cancer, categorized under head and neck cancers, requires effective screening for timely detection. This study proposes a framework, OralNet, for oral cancer detection using histopathology images. The research encompasses four stages: (i) Image collection and preprocessing, gathering and preparing histopathology images for analysis; (ii) feature extraction using deep and handcrafted scheme, extracting relevant features from images using deep learning techniques and traditional methods; (iii) feature reduction artificial hummingbird algorithm (AHA) and concatenation: Reducing feature dimensionality using AHA and concatenating them serially and (iv) binary classification and performance validation with three-fold cross-validation: Classifying images as healthy or oral squamous cell carcinoma and evaluating the framework's performance using three-fold cross-validation. The current study examined whole slide biopsy images at 100× and 400× magnifications. To establish OralNet's validity, 3000 cropped and resized images were reviewed, comprising 1500 healthy and 1500 oral squamous cell carcinoma images. Experimental results using OralNet achieved an oral cancer detection accuracy exceeding 99.5%. These findings confirm the clinical significance of the proposed technique in detecting oral cancer presence in histology slides.

Integration of IDPC Clustering Analysis and Interpretable Machine Learning for Survival Risk Prediction of Patients with ESCC

Precise forecasting of survival risk plays a pivotal role in comprehending and predicting the prognosis of patients afflicted with esophageal squamous cell carcinoma (ESCC). The existing methods have the problems of insufficient fitting ability and poor interpretability. To address this issue, this work proposes a novel interpretable survival risk prediction method for ESCC patients based on extreme gradient boosting improved by whale optimization algorithm (WOA-XGBoost) and shapley additive explanations (SHAP). Given the imbalanced nature of the data set, the adaptive synthetic sampling (ADASYN) is first used to generate the samples with high survival risk. Then, an improved clustering by fast search and find of density peaks (IDPC) algorithm based on cosine distance and K nearest neighbors is used to cluster the patients. Next, the prediction model for each cluster is obtained by WOA-XGBoost and the constructed model is visualized with SHAP to uncover the factors hidden in the structured model and improve the interpretability of the black-box model. Finally, the effectiveness of the proposed scheme is demonstrated by analyzing the data collected from the First Affiliated Hospital of Zhengzhou University. The results of the analysis reveal that the proposed methodology exhibits superior performance, as indicated by the area under the receiver operating characteristic curve (AUROC) of 0.918 and accuracy of 0.881.

Application of artificial intelligence for overall survival risk stratification in oropharyngeal carcinoma: A validation of ProgTOOL

BACKGROUND

In recent years, there has been a surge in machine learning-based models for diagnosis and prognostication of outcomes in oncology. However, there are concerns relating to the model's reproducibility and generalizability to a separate patient cohort (i.e., external validation).

OBJECTIVES

This study primarily provides a validation study for a recently introduced and publicly available machine learning (ML) web-based prognostic tool (ProgTOOL) for overall survival risk stratification of oropharyngeal squamous cell carcinoma (OPSCC). Additionally, we reviewed the published studies that have utilized ML for outcome prognostication in OPSCC to examine how many of these models were externally validated, type of external validation, characteristics of the external dataset, and diagnostic performance characteristics on the internal validation (IV) and external validation (EV) datasets were extracted and compared.

METHODS

We used a total of 163 OPSCC patients obtained from the Helsinki University Hospital to externally validate the ProgTOOL for generalizability. In addition, PubMed, OvidMedline, Scopus, and Web of Science databases were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

RESULTS

The ProgTOOL produced a predictive performance of 86.5% balanced accuracy, Mathew's correlation coefficient of 0.78, Net Benefit (0.7) and Brier score (0.06) for overall survival stratification of OPSCC patients as either low-chance or high-chance. In addition, out of a total of 31 studies found to have used ML for the prognostication of outcomes in OPSCC, only seven (22.6%) reported a form of EV. Three studies (42.9%) each used either temporal EV or geographical EV while only one study (14.2%) used expert as a form of EV. Most of the studies reported a reduction in performance when externally validated.

CONCLUSION

The performance of the model in this validation study indicates that it may be generalized, therefore, bringing recommendations of the model for clinical evaluation closer to reality. However, the number of externally validated ML-based models for OPSCC is still relatively small. This significantly limits the transfer of these models for clinical evaluation and subsequently reduces the likelihood of the use of these models in daily clinical practice. As a gold standard, we recommend the use of geographical EV and validation studies to reveal biases and overfitting of these models. These recommendations are poised to facilitate the implementation of these models in clinical practice.

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.

Deep learning: A primer for dentists and dental researchers

OBJECTIVES

Despite deep learning's wide adoption in dental artificial intelligence (AI) research, researchers from other dental fields and, more so, dental professionals may find it challenging to understand and interpret deep learning studies, their employed methods, and outcomes. The objective of this primer is to explain the basic concept of deep learning. It will lay out the commonly used terms, and describe different deep learning approaches, their methods, and outcomes.

METHODS

Our research is based on the latest review studies, medical primers, as well as the state-of-the-art research on AI and deep learning, which have been gathered in the current study.

RESULTS

In this study, a basic understanding of deep learning models and various approaches to deep learning is presented. An overview of data management strategies for deep learning projects is presented, including data collection, data curation, data annotation, and data preprocessing. Additionally, we provided a step-by-step guide for completing a real-world project.

CONCLUSION

Researchers and clinicians can benefit from this study by gaining insight into deep learning. It can be used to critically appraise existing work or plan new deep learning projects.

CLINICAL SIGNIFICANCE

This study may be useful to dental researchers and professionals who are assessing and appraising deep learning studies within the field of dentistry.

Automated Detection and Classification of Oral Squamous Cell Carcinoma Using Deep Neural Networks

This work aims to classify normal and carcinogenic cells in the oral cavity using two different approaches with an eye towards achieving high accuracy. The first approach extracts local binary patterns and metrics derived from a histogram from the dataset and is fed to several machine-learning models. The second approach uses a combination of neural networks as a backbone feature extractor and a random forest for classification. The results show that information can be learnt effectively from limited training images using these approaches. Some approaches use deep learning algorithms to generate a bounding box that can locate the suspected lesion. Other approaches use handcrafted textural feature extraction techniques and feed the resultant feature vectors to a classification model. The proposed method will extract the features pertaining to the images using pre-trained convolution neural networks (CNN) and train a classification model using the resulting feature vectors. By using the extracted features from a pre-trained CNN model to train a random forest, the problem of requiring a large amount of data to train deep learning models is bypassed. The study selected a dataset consisting of 1224 images, which were divided into two sets with varying resolutions.The performance of the model is calculated based on accuracy, specificity, sensitivity, and the area under curve (AUC). The proposed work is able to produce a highest test accuracy of 96.94% and an AUC of 0.976 using 696 images of 400× magnification and a highest test accuracy of 99.65% and an AUC of 0.9983 using only 528 images of 100× magnification images.

Advanced-stage tongue squamous cell carcinoma: a machine learning model for risk stratification and treatment planning

BACKGROUND

A significant number of tongue squamous cell carcinoma (TSCC) patients are diagnosed at late stage.

OBJECTIVES

We primarily aimed to develop a machine learning (ML) model based on ensemble ML paradigm to stratify advanced-stage TSCC patients into the likelihood of overall survival (OS) for evidence-based treatment. We compared the survival outcome of patients who received either surgical treatment only (Sx) or surgery combined with postoperative radiotherapy (Sx + RT) or postoperative chemoradiotherapy (Sx + CRT).

MATERIAL AND METHODS

A total of 428 patients from Surveillance, Epidemiology, and End Results (SEER) database were reviewed. Kaplan-Meier and Cox proportional hazards models examine OS. In addition, a ML model was developed for OS likelihood stratification.

RESULTS

Age, marital status, N stage, Sx, and Sx + CRT were considered significant. Patients with Sx + RT showed better OS than Sx + CRT or Sx alone. A similar result was obtained for T3N0 subgroup. For T3N1 subgroup, Sx + CRT appeared more favorable for 5-year OS. In T3N2 and T3N3 subgroups, the numbers of patients were small to make insightful conclusions. The OS predictive ML model showed an accuracy of 86.3% for OS likelihood prediction.

CONCLUSIONS AND SIGNIFICANCE

Patients stratified as having high likelihood of OS may be managed with Sx + RT. Further external validation studies are needed to confirm these results.

Explainable ensemble learning model improves identification of candidates for oral cancer screening

OBJECTIVES

Artificial intelligence could enhance the use of disparate risk factors (crude method) for better stratification of patients to be screened for oral cancer. This study aims to construct a meta-classifier that considers diverse risk factors to identify patients at risk of oral cancer and other suspicious oral diseases for targeted screening.

MATERIALS AND METHODS

A retrospective dataset from a community oral cancer screening program was used to construct and train the novel voting meta-classifier. Comprehensive risk factor information from this dataset was used as input features for eleven supervised learning algorithms which served as base learners and provided predicted probabilities that are weighted and aggregated by the meta-classifier. Training dataset was augmented using SMOTE-ENN. Additionally, Shapley additive explanations (SHAP) values were generated to implement the explainability of the model and display the important risk factors.

RESULTS

Our meta-classifier had an internal validation recall, specificity, and AUROC of 0.83, 0.86, and 0.85 for identifying the risk of oral cancer and 0.92, 0.60, and 0.76 for identifying suspicious oral mucosal disease respectively. Upon external validation, the meta-classifier had a significantly higher AUROC than the crude/current method used for identifying the risk of oral cancer (0.78 vs 0.46; p = 0.001) Also, the meta-classifier had better recall than the crude method for predicting the risk of suspicious oral mucosal diseases (0.78 vs 0.47).

CONCLUSION

Overall, these findings showcase that our approach optimizes the use of risk factors in identifying patients for oral screening which suggests potential clinical application.

Development and validation of machine learning-based risk prediction models of oral squamous cell carcinoma using salivary autoantibody biomarkers

INTRODUCTION

The incidence of oral cavity squamous cell carcinoma (OSCC) continues to rise. OSCC is associated with a low average survival rate, and most patients have a poor disease prognosis because of delayed diagnosis. We used machine learning techniques to predict high-risk cases of OSCC by using salivary autoantibody levels and demographic and behavioral data.

METHODS

We collected the salivary samples of patients recruited from a teaching hospital between September 2008 and December 2012. Ten salivary autoantibodies, sex, age, smoking, alcohol consumption, and betel nut chewing were used to build prediction models for identifying patients with a high risk of OSCC. The machine learning algorithms applied in the study were logistic regression, random forest, support vector machine with the radial basis function kernel, eXtreme Gradient Boosting (XGBoost), and a stacking model. We evaluated the performance of the models by using the area under the receiver operating characteristic curve (AUC), with simulations conducted 100 times.

RESULTS

A total of 337 participants were enrolled in this study. The best predictive model was constructed using a stacking algorithm with original forms of age and logarithmic levels of autoantibodies (AUC = 0.795 ± 0.055). Adding autoantibody levels as a data source significantly improved the prediction capability (from 0.698 ± 0.06 to 0.795 ± 0.055, p < 0.001).

CONCLUSIONS

We successfully established a prediction model for high-risk cases of OSCC. This model can be applied clinically through an online calculator to provide additional personalized information for OSCC diagnosis, thereby reducing the disease morbidity and mortality rates.

Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma-A Systematic Review

Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10-258) and of LNs was 340 (SD ± 268; range 21-791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43-99%) and for testing sets 86% (SD ± 5%; range 76-92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI's role in LN-classification in locally-advanced HNSCC.

Artificial Intelligence in Oral and Maxillofacial Surgery Education

Artificial intelligence has become ubiquitous with modern technology. Digital transformations are occurring in every field including medicine, surgery, and education. Computers and computer programs are getting sophisticated to form neural networks globally. These algorithms allow for sophisticated and complex pattern recognitions and make accurate predictions. This allows for both accurate diagnosis and prognostication in medicine and opens opportunities for medical and surgical education. Oral and Maxillofacial surgeons and OMS education like all of the surgery are adapting well to the world of AI, incorporating machine learning into simulation, and attaching sensors to master surgeons to understand motion economy.

Trends in Scope of Practice for Oral Health Care: Future Transformative Effects

KNOWLEDGE TRANSFER STATEMENT

The results of this study can help key stakeholders, such as health care facilities, educational and research institutions, insurance companies, and governmental bodies, plan future activities and policies on dental practice and education.

Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

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An overview of systematic reviews on the application of AI including 129 studies.

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AI use is prominent in Universal Health Coverage, featuring image analysis in neoplasms.

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Half of the reviews did not evaluate validation procedures nor reporting guidelines.

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Risk of bias was only included un a third of the reviews.

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There is not sufficient evidence to transfer AI to actual healthcare delivery.

Background

Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people’s health. It is necessary to assess the current status on the application of AI towards the improvement of people’s health in the domains defined by WHO’s Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges.

Objective

To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people’s health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality.

Methods

A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO’s PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist.

Results

The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews.

Conclusion

Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.

Accuracy of artificial intelligence-assisted detection of Oral Squamous Cell Carcinoma: A systematic review and meta-analysis

Oral Squamous Cell Carcinoma (OSCC) is an aggressive tumor with a poor prognosis. Accurate and timely diagnosis is therefore essential for reducing the burden of advanced disease and improving outcomes. In this meta-analysis, we evaluated the accuracy of artificial intelligence (AI)-assisted technologies in detecting OSCC. We included studies that validated any diagnostic modality that used AI to detect OSCC. A search was performed in six databases: PubMed, Embase, Scopus, Cochrane Library, ProQuest, and Web of Science up to 15 Mar 2022. The Quality Assessment Tool for Diagnostic Accuracy Studies was used to evaluate the included studies' quality, while the Split Component Synthesis method was utilized to quantitatively synthesize the pooled diagnostic efficacy estimates. We considered 16 out of the 566 yielded studies, which included twelve different AI models with a total of 6606 samples. The summary sensitivity, summary specificity, positive and negative likelihood ratios as well as the pooled diagnostic odds ratio were 92.0 % (95 % confidence interval [CI] 86.7-95.4 %), 91.9 % (95 % CI 86.5-95.3 %), 11.4 (95 % CI 6.74-19.2), 0.087 (95 % CI 0.051-0.146) and 132 (95 % CI 62.6-277), respectively. Our findings support the capability of AI-assisted systems to detect OSCC with high accuracy, potentially aiding the histopathological examination in early diagnosis, yet more prospective studies are needed to justify their use in the real population.

Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication

Background: Machine learning models have been reported to assist in the proper management of cancer through accurate prognostication. Integrating such models as a web-based prognostic tool or calculator may help to improve cancer care and assist clinicians in making oral cancer management-related decisions. However, none of these models have been recommended in daily practices of oral cancer due to concerns related to machine learning methodologies and clinical implementation challenges. An instance of the concerns inherent to the science of machine learning is explainability. Objectives: This study measures the usability and explainability of a machine learning-based web prognostic tool that was designed for prediction of oral tongue cancer. We used the System Usability Scale (SUS) and System Causability Scale (SCS) to evaluate the explainability of the prognostic tool. In addition, we propose a framework for the evaluation of post hoc explainability of web-based prognostic tools. Methods: A SUS- and SCS-based questionnaire was administered amongst pathologists, radiologists, cancer and machine learning researchers and surgeons (n = 11) to evaluate the quality of explanations offered by the machine learning-based web prognostic tool to address the concern of explainability and usability of these models for cancer management. The examined web-based tool was developed by our group and is freely available online. Results: In terms of the usability of the web-based tool using the SUS, 81.9% (45.5% strongly agreed; 36.4% agreed) agreed that neither the support of a technical assistant nor a need to learn many things were required to use the web-based tool. Furthermore, 81.8% agreed that the evaluated web-based tool was not cumbersome to use (usability). The average score for the SCS (explainability) was 0.74. A total of 91.0% of the participants strongly agreed that the web-based tool can assist in clinical decision-making. These scores indicated that the examined web-based tool offers a significant level of usability and explanations about the outcome of interest. Conclusions: Integrating the trained and internally and externally validated model as a web-based tool or calculator is poised to offer an effective and easy approach towards the usage and acceptance of these models in the future daily practice. This approach has received significant attention in recent years. Thus, it is important that the usability and explainability of these models are measured to achieve such touted benefits. A usable and well-explained web-based tool further brings the use of these web-based tools closer to everyday clinical practices. Thus, the concept of more personalized and precision oncology can be achieved.

High-Accuracy Oral Squamous Cell Carcinoma Auxiliary Diagnosis System Based on EfficientNet

It is important to diagnose the grade of oral squamous cell carcinoma (OSCC), but the current evaluation of the biopsy slide still mainly depends on the manual operation of pathologists. The workload of manual evaluation is large, and the results are greatly affected by the subjectivity of the pathologists. In recent years, with the development and application of deep learning, automatic evaluation of biopsy slides is gradually being applied to medical diagnoses, and it has shown good results. Therefore, a new OSCC auxiliary diagnostic system was proposed to automatically and accurately evaluate the patients’ tissue slides. This is the first study that compared the effects of different resolutions on the results. The OSCC tissue slides from The Cancer Genome Atlas (TCGA, n=697) and our independent datasets (n=337) were used for model training and verification. In the test dataset of tiles, the accuracy was 93.1% at 20x resolution (n=306,134), which was higher than that at 10x (n=154,148, accuracy=90.9%) and at 40x (n=890,681, accuracy=89.3%). The accuracy of the new system based on EfficientNet, which was used to evaluate the tumor grade of the biopsy slide, reached 98.1% [95% confidence interval (CI): 97.1% to 99.1%], and the area under the receiver operating characteristic curve (AUROC) reached 0.998 (95%CI: 0.995 to 1.000) in the TCGA dataset. When verifying the model on the independent image dataset, the accuracy still reached 91.4% (95% CI: 88.4% to 94.4%, at 20x) and the AUROC reached 0.992 (95%CI: 0.982 to 1.000). It may benefit oral pathologists by reducing certain repetitive and time-consuming tasks, improving the efficiency of diagnosis, and facilitating the further development of computational histopathology.