AIMC Topic: Mouth Neoplasms

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Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model.

BMC oral health
BACKGROUND: Postoperative fever (POF) is a common occurrence in patients undergoing major surgery, presenting challenges and burdens for both patients and surgeons yet. This study endeavors to examine the incidence, identify risk factors, and establi...

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

Scientific reports
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 artificia...

Classifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework.

Scientific reports
Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, with increasing global incidence and have poor prognosis. Tumour-infiltrating lymphocytes (TILs) are recognized as a key prognostic indicator and play a vital role in OSCC gr...

Convolutional neural networks for accurate real-time diagnosis of oral epithelial dysplasia and oral squamous cell carcinoma using high-resolution in vivo confocal microscopy.

Scientific reports
Oral cancer detection is based on biopsy histopathology, however with digital microscopy imaging technology there is real potential for rapid multi-site imaging and simultaneous diagnostic analysis. Fifty-nine patients with oral mucosal abnormalities...

ChatGPT and oral cancer: a study on informational reliability.

BMC oral health
BACKGROUND: Artificial intelligence (AI) and large language models (LLMs) like ChatGPT have transformed information retrieval, including in healthcare. ChatGPT, trained on diverse datasets, can provide medical advice but faces ethical and accuracy co...

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

Journal of environmental science and health. Part C, Toxicology and carcinogenesis
As the 16 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...

Efficacy and empathy of AI chatbots in answering frequently asked questions on oral oncology.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVES: Artificial intelligence chatbots have demonstrated feasibility and efficacy in improving health outcomes. In this study, responses from 5 different publicly available AI chatbots-Bing, GPT-3.5, GPT-4, Google Bard, and Claude-to frequently...

Diagnosis of lymph node metastasis in oral squamous cell carcinoma by an MRI-based deep learning model.

Oral oncology
BACKGROUND: Cervical lymph node metastasis (LNM) is a well-established poor prognosticator of oral squamous cell carcinoma (OSCC), in which occult metastasis is a subtype that makes prediction challenging. Here, we developed and validated a deep lear...

Attention-guided convolutional network for bias-mitigated and interpretable oral lesion classification.

Scientific reports
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for class...

Mucoepidermoid carcinoma: Enhancing diagnostic accuracy and treatment strategy through machine learning models and web-based prognostic tool.

Journal of stomatology, oral and maxillofacial surgery
BACKGROUND: Oral cancer, particularly mucoepidermoid carcinoma (MEC), presents diagnostic challenges due to its histological diversity and rarity. This study aimed to develop machine learning (ML) models to predict survival outcomes for MEC patients ...