Video Presentation Portrait
Abstract
Artificial intelligence (AI) is rapidly transforming the medical oncology, by providing new tools for early diagnosis and personalized treatment planning. In mandibular oncology, where malignant transformation and metastatic progression can drastically alter prognosis, AI-driven systems show great promise in enhancing clinical decision-making. Convolutional neural networks and deep learning algorithms have already demonstrated high accuracy in analyzing imaging datasets, including CT scans and histopathological slides, for the detection of subtle changes that may indicate malignant transformation of initially benign lesions. These tools also aid in identifying early metastatic spread, allowing for timely intervention. One of the most aggressive head and neck cancers, oral squamous cell carcinoma (OSCC) usually affects the mandible and causes extensive anatomical disruption, neurological impairments, and functional deficits in speech, mastication, and facial symmetry. This article presents clinical and histopathological data on malignant lesions of the mandibular bone, primarily oral squamous cell carcinoma (OSCC). Tumours primarily affected the mandibular body and ramus, often causing facial asymmetry, tooth mobility, and bone loss. Histologically, keratin pearls and spindle-shaped atypical cells were typical characteristics. Neurological involvement from perineural invasion frequently led to pain, numbness, and impaired oral function. In addition to the complex consequences of mandibular cancers, such as their anatomical, functional, and psychological effects, the study underscores the importance of multidisciplinary treatment and rehabilitation. Our paper presents how new methods for improving the precision and effectiveness of oral oncology diagnosis have been made possible by developments in artificial intelligence (AI), especially deep learning approaches. By recognising both cellular and structural atypia, convolutional neural networks (CNNs) have shown excellent performance in the classification of histopathological images. The distinction between benign, dysplastic, and malignant oral lesions is further enhanced by hybrid techniques that blend deep and texture-based features. AI-assisted radiomics has shown promise in diagnostic imaging by detecting high-dimensional, subtle characteristics in CT, MRI, and ultrasound scans that are frequently impossible to interpret visually. According to analysed studies, these techniques can improve the identification of lymph node metastases and offer accurate risk assessment for oral malignancies. Further encouraging their inclusion into the oral oncology workflow, machine learning models have attained diagnostic accuracies that surpass standard radiological assessments by merging image texture analysis with clinical data. A triadic viewpoint is provided by integrating AI into mandibular oncology: (1) imaging-based tumour growth and metastasis detection, (2) histopathological evaluation of malignant potential, and (3) assessment of neurological outcomes following surgery. Also, this article presents a case study from the University of Craiova, guide surgical excision, and assess both histological progression and postoperative functional impact. AI technologies' ability to support individualized treatment plans, enable earlier diagnoses, and enhance clinical judgment is becoming more and more clear as they develop—especially in anatomically and neurologically complex areas like the mandible. In conclusion, AI represents a powerful ally in mandibular oncology, bridging imaging, histopathology, and neurological evaluation, and paving the way for more precise, patient-centered care.