A novel lightweight deep convolutional neural network for early detection of oral cancer

A deep learning approach to detection of oral cancer lesions from intra oral patient images: A preliminary retrospective study

INTRODUCTION

Oral squamous cell carcinomas (OSCC) seen in the oral cavity are a category of diseases for which dentists may diagnose and even cure. This study evaluated the performance of diagnostic computer software developed to detect oral cancer lesions in intra-oral retrospective patient images.

MATERIALS AND METHODS

Oral cancer lesions were labeled with CranioCatch labeling program (CranioCatch, Eskişehir, Turkey) and polygonal type labeling method on a total of 65 anonymous retrospective intraoral patient images of oral mucosa that were diagnosed with oral cancer histopathologically by incisional biopsy from individuals in our clinic. All images have been rechecked and verified by experienced experts. This data set was divided into training (n = 53), validation (n = 6) and test (n = 6) sets. Artificial intelligence model was developed using YOLOv5 architecture, which is a deep learning approach. Model success was evaluated with confusion matrix.

RESULTS

When the success rate in estimating the images reserved for the test not used in education was evaluated, the F1, sensitivity and precision results of the artificial intelligence model obtained using the YOLOv5 architecture were found to be 0.667, 0.667 and 0.667, respectively.

CONCLUSIONS

Our study reveals that OCSCC lesions carry discriminative visual appearances, which can be identified by deep learning algorithm. Artificial intelligence shows promise in the prediagnosis of oral cancer lesions. The success rates will increase in the training models of the data set that will be formed with more images.

AI illuminates paths in oral cancer: transformative insights, diagnostic precision, and personalized strategies

Oral cancer retains one of the lowest survival rates worldwide, despite recent therapeutic advancements signifying a tenacious challenge in healthcare. Artificial intelligence exhibits noteworthy potential in escalating diagnostic and treatment procedures, offering promising advancements in healthcare. This review entails the traditional imaging techniques for the oral cancer treatment. The role of artificial intelligence in prognosis of oral cancer including predictive modeling, identification of prognostic factors and risk stratification also discussed significantly in this review. The review also encompasses the utilization of artificial intelligence such as automated image analysis, computer-aided detection and diagnosis integration of machine learning algorithms for oral cancer diagnosis and treatment. The customizing treatment approaches for oral cancer through artificial intelligence based personalized medicine is also part of this review. See also the graphical abstract(Fig. 1).

Diagnostic accuracy of artificial intelligence assisted clinical imaging in the detection of oral potentially malignant disorders and oral cancer: a systematic review and meta-analysis

BACKGROUND

The objective of this study is to examine the application of artificial intelligence (AI) algorithms in detecting oral potentially malignant disorders (OPMD) and oral cancerous lesions, and to evaluate the accuracy variations among different imaging tools employed in these diagnostic processes.

MATERIALS AND METHODS

A systematic search was conducted in four databases: Embase, Web of Science, PubMed, and Scopus. The inclusion criteria included studies using machine learning algorithms to provide diagnostic information on specific oral lesions, prospective or retrospective design, and inclusion of OPMD. Sensitivity and specificity analyses were also required. Forest plots were generated to display overall diagnostic odds ratio (DOR), sensitivity, specificity, negative predictive values, and summary receiver operating characteristic (SROC) curves. Meta-regression analysis was conducted to examine potential differences among different imaging tools.

RESULTS

The overall DOR for AI-based screening of OPMD and oral mucosal cancerous lesions from normal mucosa was 68.438 (95% CI= [39.484-118.623], I2 =86%). The area under the SROC curve was 0.938, indicating excellent diagnostic performance. AI-assisted screening showed a sensitivity of 89.9% (95% CI= [0.866-0.925]; I2 =81%), specificity of 89.2% (95% CI= [0.851-0.922], I2 =79%), and a high negative predictive value of 89.5% (95% CI= [0.851-0.927], I2 =96%). Meta-regression analysis revealed no significant difference among the three image tools. After generating a GOSH plot, the DOR was calculated to be 49.30, and the area under the SROC curve was 0.877. Additionally, sensitivity, specificity, and negative predictive value were 90.5% (95% CI [0.873-0.929], I2 =4%), 87.0% (95% CI [0.813-0.912], I2 =49%) and 90.1% (95% CI [0.860-0.931], I2 =57%), respectively. Subgroup analysis showed that clinical photography had the highest diagnostic accuracy.

CONCLUSIONS

AI-based detection using clinical photography shows a high DOR and is easily accessible in the current era with billions of phone subscribers globally. This indicates that there is significant potential for AI to enhance the diagnostic capabilities of general practitioners to the level of specialists by utilizing clinical photographs, without the need for expensive specialized imaging equipment.

Oral mucosal lesions triage via YOLOv7 models

BACKGROUND/PURPOSE

The global incidence of lip and oral cavity cancer continues to rise, necessitating improved early detection methods. This study leverages the capabilities of computer vision and deep learning to enhance the early detection and classification of oral mucosal lesions.

METHODS

A dataset initially consisting of 6903 white-light macroscopic images collected from 2006 to 2013 was expanded to over 50,000 images to train the YOLOv7 deep learning model. Lesions were categorized into three referral grades: benign (green), potentially malignant (yellow), and malignant (red), facilitating efficient triage.

RESULTS

The YOLOv7 models, particularly the YOLOv7-E6, demonstrated high precision and recall across all lesion categories. The YOLOv7-D6 model excelled at identifying malignant lesions with notable precision, recall, and F1 scores. Enhancements, including the integration of coordinate attention in the YOLOv7-D6-CA model, significantly improved the accuracy of lesion classification.

CONCLUSION

The study underscores the robust comparison of various YOLOv7 model configurations in the classification to triage oral lesions. The overall results highlight the potential of deep learning models to contribute to the early detection of oral cancers, offering valuable tools for both clinical settings and remote screening applications.

Oral mucosal disease recognition based on dynamic self-attention and feature discriminant loss

OBJECTIVES

To develop a dynamic self-attention and feature discrimination loss function (DSDF) model for identifying oral mucosal diseases presented to solve the problems of data imbalance, complex image background, and high similarity and difference of visual characteristics among different types of lesion areas.

METHODS

In DSDF, dynamic self-attention network can fully mine the context information between adjacent areas, improve the visual representation of the network, and promote the network model to learn and locate the image area of interest. Then, the feature discrimination loss function is used to constrain the diversity of channel characteristics, so as to enhance the feature discrimination ability of local similar areas.

RESULTS

The experimental results show that the recognition accuracy of the proposed method for oral mucosal disease is the highest at 91.16%, and is about 6% ahead of other advanced methods. In addition, DSDF has recall of 90.87% and F1 of 90.60%.

CONCLUSIONS

Convolutional neural networks can effectively capture the visual features of the oral mucosal disease lesions, and the distinguished visual features of different oral lesions can be extracted better using dynamic self-attention and feature discrimination loss function, which is conducive to the auxiliary diagnosis of oral mucosal diseases.

Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm

Oral cancer early diagnosis is a critical task in the field of medical science, and one of the most necessary things is to develop sound and effective strategies for early detection. The current research investigates a new strategy to diagnose an oral cancer based upon combination of effective learning and medical imaging. The current research investigates a new strategy to diagnose an oral cancer using Gated Recurrent Unit (GRU) networks optimized by an improved model of the NGO (Northern Goshawk Optimization) algorithm. The proposed approach has several advantages over existing methods, including its ability to analyze large and complex datasets, its high accuracy, as well as its capacity to detect oral cancer at the very beginning stage. The improved NGO algorithm is utilized to improve the GRU network that helps to improve the performance of the network and increase the accuracy of the diagnosis. The paper describes the proposed approach and evaluates its performance using a dataset of oral cancer patients. The findings of the study demonstrate the efficiency of the suggested approach in accurately diagnosing oral cancer.

Advancements in diagnosing oral potentially malignant disorders: leveraging Vision transformers for multi-class detection

OBJECTIVES

Diagnosing oral potentially malignant disorders (OPMD) is critical to prevent oral cancer. This study aims to automatically detect and classify the most common pre-malignant oral lesions, such as leukoplakia and oral lichen planus (OLP), and distinguish them from oral squamous cell carcinomas (OSCC) and healthy oral mucosa on clinical photographs using vision transformers.

METHODS

4,161 photographs of healthy mucosa, leukoplakia, OLP, and OSCC were included. Findings were annotated pixel-wise and reviewed by three clinicians. The photographs were divided into 3,337 for training and validation and 824 for testing. The training and validation images were further divided into five folds with stratification. A Mask R-CNN with a Swin Transformer was trained five times with cross-validation, and the held-out test split was used to evaluate the model performance. The precision, F1-score, sensitivity, specificity, and accuracy were calculated. The area under the receiver operating characteristics curve (AUC) and the confusion matrix of the most effective model were presented.

RESULTS

The detection of OSCC with the employed model yielded an F1 of 0.852 and AUC of 0.974. The detection of OLP had an F1 of 0.825 and AUC of 0.948. For leukoplakia the F1 was 0.796 and the AUC was 0.938.

CONCLUSIONS

OSCC were effectively detected with the employed model, whereas the detection of OLP and leukoplakia was moderately effective.

CLINICAL RELEVANCE

Oral cancer is often detected in advanced stages. The demonstrated technology may support the detection and observation of OPMD to lower the disease burden and identify malignant oral cavity lesions earlier.

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Enhancing oral squamous cell carcinoma detection: a novel approach using improved EfficientNet architecture

PROBLEM

Oral squamous cell carcinoma (OSCC) is the eighth most prevalent cancer globally, leading to the loss of structural integrity within the oral cavity layers and membranes. Despite its high prevalence, early diagnosis is crucial for effective treatment.

AIM

This study aimed to utilize recent advancements in deep learning for medical image classification to automate the early diagnosis of oral histopathology images, thereby facilitating prompt and accurate detection of oral cancer.

METHODS

A deep learning convolutional neural network (CNN) model categorizes benign and malignant oral biopsy histopathological images. By leveraging 17 pretrained DL-CNN models, a two-step statistical analysis identified the pretrained EfficientNetB0 model as the most superior. Further enhancement of EfficientNetB0 was achieved by incorporating a dual attention network (DAN) into the model architecture.

RESULTS

The improved EfficientNetB0 model demonstrated impressive performance metrics, including an accuracy of 91.1%, sensitivity of 92.2%, specificity of 91.0%, precision of 91.3%, false-positive rate (FPR) of 1.12%, F1 score of 92.3%, Matthews correlation coefficient (MCC) of 90.1%, kappa of 88.8%, and computational time of 66.41%. Notably, this model surpasses the performance of state-of-the-art approaches in the field.

CONCLUSION

Integrating deep learning techniques, specifically the enhanced EfficientNetB0 model with DAN, shows promising results for the automated early diagnosis of oral cancer through oral histopathology image analysis. This advancement has significant potential for improving the efficacy of oral cancer treatment strategies.

NTSM: a non-salient target segmentation model for oral mucosal diseases

BACKGROUND

Oral mucosal diseases are similar to the surrounding normal tissues, i.e., their many non-salient features, which poses a challenge for accurate segmentation lesions. Additionally, high-precision large models generate too many parameters, which puts pressure on storage and makes it difficult to deploy on portable devices.

METHODS

To address these issues, we design a non-salient target segmentation model (NTSM) to improve segmentation performance while reducing the number of parameters. The NTSM includes a difference association (DA) module and multiple feature hierarchy pyramid attention (FHPA) modules. The DA module enhances feature differences at different levels to learn local context information and extend the segmentation mask to potentially similar areas. It also learns logical semantic relationship information through different receptive fields to determine the actual lesions and further elevates the segmentation performance of non-salient lesions. The FHPA module extracts pathological information from different views by performing the hadamard product attention (HPA) operation on input features, which reduces the number of parameters.

RESULTS

The experimental results on the oral mucosal diseases (OMD) dataset and international skin imaging collaboration (ISIC) dataset demonstrate that our model outperforms existing state-of-the-art methods. Compared with the nnU-Net backbone, our model has 43.20% fewer parameters while still achieving a 3.14% increase in the Dice score.

CONCLUSIONS

Our model has high segmentation accuracy on non-salient areas of oral mucosal diseases and can effectively reduce resource consumption.

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.

Enhancing deep learning classification performance of tongue lesions in imbalanced data: mosaic-based soft labeling with curriculum learning

BACKGROUND

Oral potentially malignant disorders (OPMDs) are associated with an increased risk of cancer of the oral cavity including the tongue. The early detection of oral cavity cancers and OPMDs is critical for reducing cancer-specific morbidity and mortality. Recently, there have been studies to apply the rapidly advancing technology of deep learning for diagnosing oral cavity cancer and OPMDs. However, several challenging issues such as class imbalance must be resolved to effectively train a deep learning model for medical imaging classification tasks. The aim of this study is to evaluate a new technique of artificial intelligence to improve the classification performance in an imbalanced tongue lesion dataset.

METHODS

A total of 1,810 tongue images were used for the classification. The class-imbalanced dataset consisted of 372 instances of cancer, 141 instances of OPMDs, and 1,297 instances of noncancerous lesions. The EfficientNet model was used as the feature extraction model for classification. Mosaic data augmentation, soft labeling, and curriculum learning (CL) were employed to improve the classification performance of the convolutional neural network.

RESULTS

Utilizing a mosaic-augmented dataset in conjunction with CL, the final model achieved an accuracy rate of 0.9444, surpassing conventional oversampling and weight balancing methods. The relative precision improvement rate for the minority class OPMD was 21.2%, while the relative [Formula: see text] score improvement rate of OPMD was 4.9%.

CONCLUSIONS

The present study demonstrates that the integration of mosaic-based soft labeling and curriculum learning improves the classification performance of tongue lesions compared to previous methods, establishing a foundation for future research on effectively learning from imbalanced data.

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.

Multimodal feature fusion in deep learning for comprehensive dental condition classification

BACKGROUND

Dental health issues are on the rise, necessitating prompt and precise diagnosis. Automated dental condition classification can support this need.

OBJECTIVE

The study aims to evaluate the effectiveness of deep learning methods and multimodal feature fusion techniques in advancing the field of automated dental condition classification.

METHODS AND MATERIALS

A dataset of 11,653 clinically sourced images representing six prevalent dental conditions-caries, calculus, gingivitis, tooth discoloration, ulcers, and hypodontia-was utilized. Features were extracted using five Convolutional Neural Network (CNN) models, then fused into a matrix. Classification models were constructed using Support Vector Machines (SVM) and Naive Bayes classifiers. Evaluation metrics included accuracy, recall rate, precision, and Kappa index.

RESULTS

The SVM classifier integrated with feature fusion demonstrated superior performance with a Kappa index of 0.909 and accuracy of 0.925. This significantly surpassed individual CNN models such as EfficientNetB0, which achieved a Kappa of 0.814 and accuracy of 0.847.

CONCLUSIONS

The amalgamation of feature fusion with advanced machine learning algorithms can significantly bolster the precision and robustness of dental condition classification systems. Such a method presents a valuable tool for dental professionals, facilitating enhanced diagnostic accuracy and subsequently improved patient outcomes.

[Oral cavity cancer: A distinct entity]

Oral Squamous cell carcinoma represent the 17th most frequent cancer in the world. The main risk factors are alcohol and tobacco consumption but dietary, familial, genetic, or oral diseases may be involved in oral carcinogenesis. Diagnosis is made on biopsy, but detection remains late, leading to a poor prognosis. New technologies could reduce these delays, notably Artificial Intelligence and the quantitative evaluation of salivary biological markers. Currently, management of oral cancer consists in surgery, which can be mutilating despite possible reconstructions. In the future, immunotherapies could become a therapeutic alternative and the immune microenvironment could constitute a source of prognostic markers.

The preliminary in vitro study and application of deep learning algorithm in cone beam computed tomography image implant recognition

To properly repair and maintain implants, which are bone tissue implants that replace natural tooth roots, it is crucial to accurately identify their brand and specification. Deep learning has demonstrated outstanding capabilities in analysis, such as image identification and classification, by learning the inherent rules and degrees of representation of data models. The purpose of this study is to evaluate deep learning algorithms and their supporting application software for their ability to recognize and categorize three dimensional (3D) Cone Beam Computed Tomography (CBCT) images of dental implants. By using CBCT technology, the 3D imaging data of 27 implants of various sizes and brands were obtained. Following manual processing, the data were transformed into a data set that had 13,500 two-dimensional data. Nine deep learning algorithms including GoogleNet, InceptionResNetV2, InceptionV3, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152 and ResNet152V2 were used to perform the data. Accuracy rates, confusion matrix, ROC curve, AUC, number of model parameters and training times were used to assess the efficacy of these algorithms. These 9 deep learning algorithms achieved training accuracy rates of 100%, 99.3%, 89.3%, 99.2%, 99.1%, 99.5%, 99.4%, 99.5%, 98.9%, test accuracy rates of 98.3%, 97.5%, 94.8%, 85.4%, 92.5%, 80.7%, 93.6%, 93.2%, 99.3%, area under the curve (AUC) values of 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00. When used to identify implants, all nine algorithms perform satisfactorily, with ResNet152V2 achieving the highest test accuracy, classification accuracy, confusion matrix area under the curve, and receiver operating characteristic curve area under the curve area. The results showed that the ResNet152V2 has the best classification effect on identifying implants. The artificial intelligence identification system and application software based on this algorithm can efficiently and accurately identify the brands and specifications of 27 classified implants through processed 3D CBCT images in vitro, with high stability and low recognition cost.

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.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning

OBJECTIVE

The present study aims to quantify clinicians' perceptions of oral potentially malignant disorders (OPMDs) when evaluating, classifying, and manually annotating clinical images, as well as to understand the source of inter-observer variability when assessing these lesions. The hypothesis was that different interpretations could affect the quality of the annotations used to train a Supervised Learning model.

STUDY DESIGN

Forty-six clinical images from 37 patients were reviewed, classified, and manually annotated at the pixel level by 3 labelers. We compared the inter-examiner assessment based on clinical criteria through the κ statistics (Fleiss's kappa). The segmentations were also compared using the mean pixel-wise intersection over union (IoU).

RESULTS

The inter-observer agreement for homogeneous/non-homogeneous criteria was substantial (κ = 63, 95% CI: 0.47-0.80). For the subclassification of non-homogeneous lesions, the inter-observer agreement was moderate (κ = 43, 95% CI: 0.34-0.53) (P < .001). The mean IoU of 0.53 (±0.22) was considered low.

CONCLUSION

The subjective clinical assessment (based on human visual observation, variable criteria that have suffered adjustments over the years, different educational backgrounds, and personal experience) may explain the source of inter-observer discordance for the classification and annotation of OPMD. Therefore, there is a strong probability of transferring the subjectivity of human analysis to artificial intelligence models. The use of large data sets and segmentation based on the union of all labelers' annotations holds the potential to overcome this limitation.

Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review

Oral potentially malignant disorders represent precursor lesions that may undergo malignant transformation to oral cancer. There are many known risk factors associated with the development of oral potentially malignant disorders, and contribute to the risk of malignant transformation. Although many advances have been reported to understand the biological behavior of oral potentially malignant disorders, their clinical features that indicate the characteristics of malignant transformation are not well established. Early diagnosis of malignancy is the most important factor to improve patients' prognosis. The integration of machine learning into routine diagnosis has recently emerged as an adjunct to aid clinical examination. Increased performances of artificial intelligence AI-assisted medical devices are claimed to exceed the human capability in the clinical detection of early cancer. Therefore, the aim of this narrative review is to introduce artificial intelligence terminology, concepts, and models currently used in oncology to familiarize oral medicine scientists with the language skills, best research practices, and knowledge for developing machine learning models applied to the clinical detection of oral potentially malignant disorders.

Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach

INTRODUCTION

Artificial intelligence models and networks can learn and process dense information in a short time, leading to an efficient, objective, and accurate clinical and histopathological analysis, which can be useful to improve treatment modalities and prognostic outcomes. This paper targets oral pathologists, oral medicinists, and head and neck surgeons to provide them with a theoretical and conceptual foundation of artificial intelligence-based diagnostic approaches, with a special focus on convolutional neural networks, the state-of-the-art in artificial intelligence and deep learning.

METHODS

The authors conducted a literature review, and the convolutional neural network's conceptual foundations and functionality were illustrated based on a unique interdisciplinary point of view.

CONCLUSION

The development of artificial intelligence-based models and computer vision methods for pattern recognition in clinical and histopathological image analysis of head and neck cancer has the potential to aid diagnosis and prognostic prediction.

Temporomandibular Joint Osteoarthritis Diagnosis Employing Artificial Intelligence: Systematic Review and Meta-Analysis

The aim was to systematically synthesize the current research and influence of artificial intelligence (AI) models on temporomandibular joint (TMJ) osteoarthritis (OA) diagnosis using cone-beam computed tomography (CBCT) or panoramic radiography. Seven databases (PubMed, Embase, Scopus, Web of Science, LILACS, ProQuest, and SpringerLink) were searched for TMJ OA and AI articles. We used QUADAS-2 to assess the risk of bias, while with MI-CLAIM we checked the minimum information about clinical artificial intelligence modeling. Two hundred and three records were identified, out of which seven were included, amounting to 10,077 TMJ images. Three studies focused on the diagnosis of TMJ OA using panoramic radiography with various transfer learning models (ResNet model) on which the meta-analysis was performed. The pooled sensitivity was 0.76 (95% CI 0.35-0.95) and the specificity was 0.79 (95% CI 0.75-0.83). The other studies investigated the 3D shape of the condyle and disease classification observed on CBCT images, as well as the numerous radiomics features that can be combined with clinical and proteomic data to investigate the most effective models and promising features for the diagnosis of TMJ OA. The accuracy of the methods was nearly equivalent; it was higher when the indeterminate diagnosis was excluded or when fine-tuning was used.

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

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

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

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

Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach

Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.

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

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

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

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

Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis

Simple Summary

Early detection of oral cancer is important to increase the survival rate and reduce morbidity. For the past few years, the early detection of oral cancer using artificial intelligence (AI) technology based on autofluorescence imaging, photographic imaging, and optical coherence tomography imaging has been an important research area. In this study, diagnostic values including sensitivity and specificity data were comprehensively confirmed in various studies that performed AI analysis of images. The diagnostic sensitivity of AI-assisted screening was 0.92. In subgroup analysis, there was no statistically significant difference in the diagnostic rate according to each image tool. AI shows good diagnostic performance with high sensitivity for oral cancer. Image analysis using AI is expected to be used as a clinical tool for early detection and evaluation of treatment efficacy for oral cancer.

Abstract

The accuracy of artificial intelligence (AI)-assisted discrimination of oral cancerous lesions from normal mucosa based on mucosal images was evaluated. Two authors independently reviewed the database until June 2022. Oral mucosal disorder, as recorded by photographic images, autofluorescence, and optical coherence tomography (OCT), was compared with the reference results by histology findings. True-positive, true-negative, false-positive, and false-negative data were extracted. Seven studies were included for discriminating oral cancerous lesions from normal mucosa. The diagnostic odds ratio (DOR) of AI-assisted screening was 121.66 (95% confidence interval [CI], 29.60; 500.05). Twelve studies were included for discriminating all oral precancerous lesions from normal mucosa. The DOR of screening was 63.02 (95% CI, 40.32; 98.49). Subgroup analysis showed that OCT was more diagnostically accurate (324.33 vs. 66.81 and 27.63) and more negatively predictive (0.94 vs. 0.93 and 0.84) than photographic images and autofluorescence on the screening for all oral precancerous lesions from normal mucosa. Automated detection of oral cancerous lesions by AI would be a rapid, non-invasive diagnostic tool that could provide immediate results on the diagnostic work-up of oral cancer. This method has the potential to be used as a clinical tool for the early diagnosis of pathological lesions.

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.

Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications

With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices, through wired or wireless mediums to form a network. There are numerous security vulnerabilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) technology is a decentralized and distributed ledger that comes in handy in storing patient data and encountering data integrity and confidentiality challenges. Therefore, it is a viable solution for addressing existing IoT data security and privacy challenges. BC paves a tremendous path to revolutionize traditional IoT systems by enhancing data security, privacy, and transparency. The scientific community has shown a variety of healthcare applications based on artificial intelligence (AI) that improve health diagnosis and monitoring practices. Moreover, technology companies and startups are revolutionizing healthcare with AI and related technologies. This study illustrates the implication of integrated technologies based on BC, IoT, and AI to meet growing healthcare challenges. This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector. In addition, our research study presents a detailed survey on enabling technologies for the futuristic, intelligent, and secure internet of health things (IoHT). Furthermore, this study comprehensively studies the peculiarities of the IoHT environment and the security, performance, and progression of the enabling technologies. First, the research gaps are identified by mapping security and performance benefits inferred by the BC technologies. Secondly, practical issues related to the integration process of BC and IoT devices are discussed. Third, the healthcare applications integrating IoT, BC, and ML in healthcare environments are discussed. Finally, the research gaps, future directions, and limitations of the enabling technologies are discussed.

Detecting Oral Cancer: The Potential of Artificial Intelligence

BACKGROUND

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

OBJECTIVE

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

METHODS

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

CONCLUSION

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

Intelligent systems for precision dental diagnosis and treatment planning – A review

Machines have changed the course of mankind. Simple machines were the basis of human civilization. Today with humongous technological development, machines are intelligent enough to carry out very complex nerve-racking tasks. The ability of a machine to learn from algorithms changed eventually into, the machine learning by itself, which constitutes artificial intelligence. Literature has plausible evidence for the use of intelligent systems in medical field. Artificial intelligence has been used in the multiple denominations of dentistry. These machines are used in the precision diagnosis, interpretation of medical images, accumulation of data, classification and compilation of records, determination of treatment and construction of a personalized treatment plan. Artificial intelligence can help in timely diagnosis of complex dental diseases which would ultimately aid in rapid commencement of treatment. Research helps us understand the effectiveness and challenges in the use of this technology. The apt use of intelligent systems could transform the entire medical system for the better.

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

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

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

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.

Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism

Computational intelligence has been widely used in medical information processing. The deep learning methods, especially, have many successful applications in medical image analysis. In this paper, we proposed an end-to-end medical lesion segmentation framework based on convolutional neural networks with a dual attention mechanism, which integrates both fully and weakly supervised segmentation. The weakly supervised segmentation module achieves accurate lesion segmentation by using bounding-box labels of lesion areas, which solves the problem of the high cost of pixel-level labels with lesions in the medical images. In addition, a dual attention mechanism is introduced to enhance the network’s ability for visual feature learning. The dual attention mechanism (channel and spatial attention) can help the network pay attention to feature extraction from important regions. Compared with the current mainstream method of weakly supervised segmentation using pseudo labels, it can greatly reduce the gaps between ground-truth labels and pseudo labels. The final experimental results show that our proposed framework achieved more competitive performances on oral lesion dataset, and our framework further extended to dermatological lesion segmentation.

Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy

Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate dysplastic and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy imaging of cancerous and dysplastic oral lesions was performed at two clinical centers. Endoscopic maFLIM images from 34 patients acquired at one of the clinical centers were used to optimize ML models for automated discrimination of dysplastic and cancerous from healthy oral tissue. A computer-aided detection system was developed and applied to a set of endoscopic maFLIM images from 23 patients acquired at the other clinical center, and its performance was quantified in terms of the area under the receiver operating characteristic curve (ROC-AUC). Discrimination of dysplastic and cancerous from healthy oral tissue was achieved with an ROC-AUC of 0.81. This study demonstrates the capabilities of widefield maFLIM endoscopy to clinically image autofluorescence biomarkers that can be used in ML models to discriminate dysplastic and cancerous from healthy oral tissue. Widefield maFLIM endoscopy thus holds potential for automated in situ detection of oral dysplasia and cancer.

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

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