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.

As in any other field, medicine is advancing and striving to adapt to increasingly complex demands, aiming to find rapid solutions in establishing certain diagnoses that can save or significantly improve the patient's quality of life. The integration of AI systems in the medical field is vital and requires openness, as well as a continuous desire for learning and improvement. Although the use of artificial intelligence in the medical field in Romania has not yet reached widespread development, there are still some AI software and applications that prove their effectiveness and ease the work of medical specialists. Oral cancer is one of the most common forms of malignant or premalignant lesions globally, with a high prevalence. Although developed countries are less affected compared to developing ones, they are also seeing an increasing incidence of cancers in the oro-maxillofacial region. The etiology of oral cancer is complex, influenced by both genetic and epigenetic factors. Currently, research is increasingly focused on epigenetic factors, including smoking, chronic alcoholism, diet, and improper use of mouthwash. These behaviours are associated with significant risks for the development of oral cancer, highlighting the importance of awareness and prevention within the global population. In Romania, factors such as low dental visit frequency, poor infrastructure, and lack of education contribute to disease prevalence. According to GLOBOCAN data, oral cancer in Romania ranks second in incidence, after leukemia, with a rate of 5.6 per 100,000 individuals. The purpose of our study is to highlight the importance of histopathological examination in establishing a definitive diagnosis, as well as the significance of AI software used in storing and processing the data obtained in the laboratory.

Background/Objectives: Over the years, the potential of the first-trimester (FT) ultrasound in the detection of fetal structural defects has increased. The main objectives of the first-trimester fetal screening evaluation are the detection of major structural anomalies and the diagnosis of additional sonographic markers for chromosomal disorders. When a fetal anomaly is diagnosed, patients have the right to be informed about the risks, necessary interventions, or alternatives. Depending on the severity of the anomalies and the pregnancy period, the legality of the pregnancy termination was evaluated. The aim of this study was to assess the impact of the first-trimester morphological screening of the fetus using an ultrasound protocol according to the latest international protocols (the ISUOG protocol). Methods: Between 1 January 2024 and 31 December 2024, 854 pregnancies with gestational ages between 11 weeks and 13 weeks + 6 days were morphologically evaluated during the nuchal scan in the Obstetrics and Gynecology Department of the Emergency County Hospital from Craiova. Both transabdominal and transvaginal ultrasound in 2D and in a color Doppler mode were used in the scanning technique. The ultrasound findings were correlated with the genetic testing results and pregnancy outcome. The medical law implications were related to the cases where the ultrasound was performed at about 13 weeks of gestation, and the screening genetic results showed an increased pregnancy risk, which arose during the FT. In these cases, we performed amniocentesis at about 16–17 weeks of gestation, and especially, the Non-Invasive Prenatal Testing (NIPT)-positive cases were confirmed by karyotyping. Still, at this gestational age of diagnosis, the Romanian law would not allow abortions. Results: By using this extended FT ultrasound protocol, we detected 58 cases with fetal structural anomalies. Eighteen cases were also associated with genetic syndromes after performing chorionic villous sampling (CVS). Three cases detected with minor structural anomalies (two cases with club foot and one case with a cleft upper lip) were lost to follow-up. Conclusions: Fetal morphological ultrasound evaluation is feasible in the late first trimester. By using an extended ultrasound protocol, we can detect most of the fetal structural anomalies and contribute to better medical counseling and improve pregnancy outcomes.

Aim of the study The purpose of our study is to present the clinical aspects of gingival overgrowth induced by orthodontic therapy and to highlight the tissue changes of this gingival lesion. Materials and methods Biopsies of the gingival mucosa were taken from subjects diagnosed with gingival overgrowth and who were undergoing orthodontic therapy. The gingival mucosa samples were processed in the histology laboratory of University of Medicine and Pharmacy Craiova and the usual histological staining (hematoxylin-eosin) was performed. Results Gingival overgrowth (GO) is a condition characterized by an increase in gingival size, often observed in patients undergoing orthodontic therapy. The gingival enlargement begins o two or three months after the insertion of orthodontic appliance. The main causes of gingival overgrowth during orthodontic treatment include increased plaque accumulation due to challenges in maintaining oral hygiene with fixed appliances. Another cause is represented by the excessive pressure exerted by the orthodontic forces on the periodontium.Various factors influence the development of gingival overgrowth, including gender, age, and duration of orthodontic treatment. Conclusions Gingival overgrowth of orthodontic etiology can take on various clinical and morphological aspects and depends on the status of oral hygiene, the duration and correctness of orthodontic therapy.

Introduction The heterogeneity of head and neck cancers poses challenges in treatment planning, necessitating a nuanced approach.Materials and methods We have carried out a literature review that highlights the risk factors in the onset of oral and head and neck cancers. We also addressed the therapeutic aspects and the influence of epigenetics.Results In identifying risk factors, our literature research highlights systemic causes such as age, gender, heredity, race, socio-economic status, and immunodeficiency. Local causes, including tobacco and alcohol use, chronic traumas, chronic infections, and the role of viruses, underscore the multifactorial nature of oral cancer etiology. Addressing prevention strategies, our review underscores the potential of probiotics in suppressing oral cancer cell proliferation, with specific strains demonstrating efficacy in reducing mucositis caused by radiotherapy and chemotherapy. Additionally, the role of vitamins, omega-3 fatty acids, and compounds like curcumin in chemoprevention is explored. The Mediterranean diet emerges as a potential preventive measure, given its association with reduced cancer risk due to its rich content of monounsaturated fatty acids, antioxidants, and dietary fiber. Conclusions In conclusion, the intricate interplay of various risk factors underscores the multifactorial nature of oral cancer, with systemic and local influences contributing to its onset and progression. Age, gender, heredity, race, socio-economic status, immunodeficiency, and lifestyle choices such as tobacco, alcohol, and poor oral hygiene collectively elevate the risk. Specific chronic infections, the role of viruses, and even certain medications further contribute to the complexity of this disease.

Pleomorphic adenoma (PA) is the most common benign tumour of the salivary glands, accounting for up to two-thirds of all salivary gland tumors. It most frequently occurs in the parotid gland and typically presents as a slow-growing, asymptomatic mass, predominantly affecting females between the third and sixth decades of life. Histologically, PA exhibits a mixed composition of epithelial, myoepithelial, and mesenchymal components embedded in a myxoid or chondroid stroma, with eosinophilic cytoplasm in polygonal myoepithelial cells. Although often well-circumscribed and localized in the superficial lobe of the parotid, PA can extend into deeper regions, including the parapharyngeal space, which complicates surgical management due to the proximity of critical neurovascular structures. This study aims to highlight the histopathological features of pleomorphic adenoma and correlate them with intraoperative findings. Advanced imaging techniques, particularly MRI, are crucial for accurate preoperative assessment, as delayed surgical intervention increases the risk of malignant transformation into carcinoma ex-pleomorphic adenoma, a more aggressive entity associated with recurrence and metastasis. In recent years, machine learning and deep learning models applied to MRI and ultrasound imaging have demonstrated significant potential in differentiating pleomorphic adenoma from other benign and malignant parotid tumours, improving diagnostic accuracy and supporting earlier clinical decision-making. Microscopic findings confirmed characteristic features of PA, including epithelial proliferation, myoepithelial components, haemorrhagic zones, eosinophilic stroma, and incomplete tumour capsules. Additionally, recent AI-assisted radiomic and convolutional neural network approaches have been shown to complement classical histopathology by extracting high-dimensional imaging features that enhance tumour subtype classification and prognostic assessment. When integrated with clinical and imaging data, these machine learning techniques offer promising avenues for reducing diagnostic uncertainty, optimising surgical planning—particularly in relation to facial nerve preservation—and contributing to more personalised management strategies in parotid gland pathology.