Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review

Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia

Visual diagnosis is one of the key features of squamous cell carcinoma of the oral cavity (OSCC) and oropharynx (OPSCC), both subsets of head and neck squamous cell carcinoma (HNSCC) with a heterogeneous clinical appearance. Advancements in artificial intelligence led to Image recognition being introduced recently into large language models (LLMs) such as ChatGPT 4.0. This exploratory study, for the first time, evaluated the application of image recognition by ChatGPT to diagnose squamous cell carcinoma and leukoplakia based on clinical images, with images without any lesion as a control group. A total of 45 clinical images were analyzed, comprising 15 cases each of SCC, leukoplakia, and non-lesion images. ChatGPT 4.0 was tasked with providing the most likely diagnosis based on these images in scenario one. In scenario two the image and the clinical history were provided, whereas in scenario three only the clinical history was given. The results and the accuracy of the LLM were rated by two independent reviewers and the overall performance was evaluated using the modified Artificial Intelligence Performance Index (AIPI. In this study, ChatGPT 4.0 demonstrated the ability to correctly identify leukoplakia cases using image recognition alone, while the ability to diagnose SCC was insufficient, but improved by including the clinical history in the prompt. Providing only the clinical history resulted in a misclassification of most leukoplakia and some SCC cases. Oral cavity lesions were more likely to be diagnosed correctly. In this exploratory study of 45 images of oral lesions, ChatGPT 4.0 demonstrated a convincing performance for detecting SCC only when the clinical history was added, whereas Leukoplakia was detected solely by image recognition. ChatGPT is therefore currently insufficient for reliable OPSCC and OSCC diagnosis, but further technological advancements may pave the way for the use in the clinical setting.

Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications

Background/Objectives: Oral cancer, the sixth most common cancer worldwide, is linked to smoke, alcohol, and HPV. This scoping analysis summarized early-onset oral cancer diagnosis applications to address a gap. Methods: A scoping review identified, selected, and synthesized AI-based oral cancer diagnosis, screening, and prognosis literature. The review verified study quality and relevance using frameworks and inclusion criteria. A full search included keywords, MeSH phrases, and Pubmed. Oral cancer AI applications were tested through data extraction and synthesis. Results: AI outperforms traditional oral cancer screening, analysis, and prediction approaches. Medical pictures can be used to diagnose oral cancer with convolutional neural networks. Smartphone and AI-enabled telemedicine make screening affordable and accessible in resource-constrained areas. AI methods predict oral cancer risk using patient data. AI can also arrange treatment using histopathology images and address data heterogeneity, restricted longitudinal research, clinical practice inclusion, and ethical and legal difficulties. Future potential includes uniform standards, long-term investigations, ethical and regulatory frameworks, and healthcare professional training. Conclusions: AI may transform oral cancer diagnosis and treatment. It can develop early detection, risk modelling, imaging phenotypic change, and prognosis. AI approaches should be standardized, tested longitudinally, and ethical and practical issues related to real-world deployment should be addressed.

Advanced deep learning algorithms in oral cancer detection: Techniques and applications

As the 16th most common cancer globally, oral cancer yearly accounts for some 355,000 new cases. This study underlines that an early diagnosis can improve the prognosis and cut down on mortality. It discloses a multifaceted approach to the detection of oral cancer, including clinical examination, biopsies, imaging techniques, and the incorporation of artificial intelligence and deep learning methods. This study is distinctive in that it provides a thorough analysis of the most recent AI-based methods for detecting oral cancer, including deep learning models and machine learning algorithms that use convolutional neural networks. By improving the precision and effectiveness of cancer cell detection, these models eventually make early diagnosis and therapy possible. This study also discusses the importance of techniques in image pre-processing and segmentation in improving image quality and feature extraction, an essential component of accurate diagnosis. These techniques have shown promising results, with classification accuracies reaching up to 97.66% in some models. Integrating the conventional methods with the cutting-edge AI technologies, this study seeks to advance early diagnosis of oral cancer, thus enhancing patient outcomes and cutting down on the burden this disease is imposing on healthcare systems.

Assessing Artificial Intelligence in Oral Cancer Diagnosis: A Systematic Review

BACKGROUND

With the use of machine learning algorithms, artificial intelligence (AI) has become a viable diagnostic and treatment tool for oral cancer. AI can assess a variety of information, including histopathology slides and intraoral pictures.

AIM

The purpose of this systematic review is to evaluate the efficacy and accuracy of AI technology in the detection and diagnosis of oral cancer between 2020 and 2024.

METHODOLOGY

With an emphasis on AI applications in oral cancer diagnostics, a thorough search approach was used to find pertinent publications published between 2020 and 2024. Using particular keywords associated with AI, oral cancer, and diagnostic imaging, databases such as PubMed, Scopus, and Web of Science were searched. Among the selection criteria were actual English-language research papers that assessed the effectiveness of AI models in diagnosing oral cancer. Three impartial reviewers extracted data, evaluated quality, and compiled the findings using a narrative synthesis technique.

RESULTS

Twelve papers that demonstrated a range of AI applications in the diagnosis of oral cancer satisfied the inclusion criteria. This study showed encouraging results in lesion identification and prognostic prediction using machine learning and deep learning algorithms to evaluate oral pictures and histopathology slides. The results demonstrated how AI-driven technologies might enhance diagnostic precision and enable early intervention in cases of oral cancer.

CONCLUSION

Unprecedented prospects to transform oral cancer diagnosis and detection are provided by artificial intelligence. More resilient AI systems in oral oncology can be achieved by joint research and innovation efforts, even in the face of constraints like data set variability and regulatory concerns.

Epidemiology, Diagnostics, and Therapy of Oral Cancer-Update Review

Oral cavity and lip cancers are the 16th most common cancer in the world. It is widely known that a lack of public knowledge about precancerous lesions, oral cancer symptoms, and risk factors leads to diagnostic delay and therefore a lower survival rate. Risk factors, which include drinking alcohol, smoking, HPV infection, a pro-inflammatory factor-rich diet, and poor oral hygiene, must be known and avoided by the general population. Regular clinical oral examinations should be enriched in an oral cancer search protocol for the most common symptoms, which are summarized in this review. Moreover, new diagnostic methods, some of which are already available (vital tissue staining, optical imaging, oral cytology, salivary biomarkers, artificial intelligence, colposcopy, and spectroscopy), and newly researched techniques increase the likelihood of stopping the pathological process at a precancerous stage. Well-established oral cancer treatments (surgery, radiotherapy, chemotherapy, and immunotherapy) are continuously being developed using novel technologies, increasing their success rate. Additionally, new techniques are being researched. This review presents a novel glance at oral cancer-its current classification and epidemiology-and will provide new insights into the development of new diagnostic methods and therapies.

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.

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 Compared to Biopsy in Detecting Early Oral Squamous Cell Carcinoma: A Systematic Review and Meta Analysis

OBJECTIVE

To summarize and compare the existing evidence on diagnostic accuracy of artificial intelligence (AI) models in detecting early oral squamous cell carcinoma (OSCC).

METHOD

Review was performed in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis - Diagnostic Test Accuracy (PRISMA- DTA) checklist and the review protocol is registered under PROSPERO(CRD42023456355). PubMed, Google Scholar, EBSCOhost were searched from January 2000 to November 2023 to identify the diagnostic potential of AI based tools and models. True-positive, false-positive, true-negative, false-negative, sensitivity, specificity values were extracted or calculated if not present for each study. Quality of selected studies was evaluated based on QUADAS (Quality assessment of diagnostic accuracy studies)- 2 tool. Meta-analysis was performed in Meta-Disc 1.4 software and Review Manager 5.3 RevMan using a bivariate model parameter for the sensitivity and specificity and summary points, summary receiver operating curve (SROC), diagnostic odds ratio (DOR) confidence region, and area under curve (AUC) were calculated.

RESULTS

Fourteen studies were included for qualitative synthesis and for meta-analysis. Included studies had presence of low to moderate risk of bias. Pooled sensitivity and specificity of 0.43 (CI 0.18- 0.71) and 0.50 (CI 0.20- 0.80) was observed with a pooled positive likelihood ratio of (PLR) 0.86 (0.43 - 1.71) and negative likelihood ratio (NLR) of 1.04 (0.42 - 1.68) was observed with DOR of 0.78 (0.12 - 5.18) and overall accuracy (AUC) being 0.45 respectively.

CONCLUSION

AI based tools has poor to moderate overall diagnostic accuracy. However, to validate our study findings further more standardized diagnostic accuracy studies should be conducted with proper reporting through QUADAS-2 tool. Thus, we can conclude AI based based tool for secondary level of prevention for early OSCC under early diagnosis and prompt treatment.

Artificial intelligence in head and neck surgery: Potential applications and future perspectives

Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wide range of data, including images, voice, molecular expression and raw clinical data. In the field of oncology, there are numerous AI practical applications, including diagnostics and treatment. AI can also develop predictive models to assess prognosis, overall survival, the likelihood of occult metastases, risk of complications and hospital length of stay.

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.

Machine Learning in the Detection of Oral Lesions With Clinical Intraoral Images

INTRODUCTION

Artificial intelligence in oncology has gained a lot of interest in recent years. Early detection of Oral squamous cell carcinoma (OSCC) is crucial for early management to attain a better prognosis and overall survival. Machine learning (ML) has also been used in oral cancer studies to explore the discrimination between clinically normal and oral cancer.

MATERIALS AND METHODS

A dataset comprising 360 clinical intra-oral images of OSCC, Oral Potentially Malignant Disorders (OPMDs) and clinically healthy oral mucosa were used. Clinicians trained the machine learning model with the clinical images (n=300). Roboflow software (Roboflow Inc, USA) was used to classify and annotate images along with Multi-class annotation and object detection models trained by two expert oral pathologists. The test dataset (n=60) of new clinical images was again evaluated by two clinicians and Roboflow. The results were tabulated and Kappa statistics was performed using SPSS v23.0 (IBM Corp., Armonk, NY).  Results: Training dataset clinical images (n=300) were used to train the clinicians and Roboflow algorithm. The test dataset (n=60) of new clinical images was again evaluated by the clinicians and Roboflow. The observed outcomes revealed that the Mean Average Precision (mAP) was 25.4%, precision 29.8% and Recall 32.9%. Based on the kappa statistical analysis the 0.7 value shows a moderate agreement between the clinicians and the machine learning model. The test dataset showed the specificity and sensitivity of the Roboflow machine learning model to be 75% and 88.9% respectively.  Conclusion: In conclusion, machine learning showed promising results in the early detection of suspected lesions using clinical intraoral images and aids general dentists and patients in the detection of suspected lesions such as OPMDs and OSCC that require biopsy and immediate treatment.