Data mining is the upcoming research area to solve various problems. Classification and finding association are two main steps in the field of data mining. In this paper, we use three classification algorithms: J48 (an open source Java implementation of C4.5 algorithm), Multilayer Perceptron - MLP (a modification of the standard linear perceptron) and Naïve Bayes (based on Bayes rule and a set of conditional independence assumptions) of the Weka interface. These classifiers have been used to choose the best algorithm based on the conditions of the voice disorders database. To find association rules over transactional medical database first we use apriori algorithm for frequent item set mining. These two initial steps of analysis will help to create the medical knowledgebase. The ultimate goal is to build a model, which can improve the way to read and interpret the existing data in medical database and future data as well.

In recent years, machine learning (ML) has been increasingly used in many fields, including medicine. Magnetic resonance imaging (MRI) is a non-invasive and effective diagnostic technique; however, manual image analysis is time-consuming and prone to human variability. In response, ML models have been developed to support MRI analysis, particularly in segmentation and classification tasks. This work presents an updated narrative review of ML applications in brain MRI, with a focus on tumor classification and segmentation. A literature search was conducted in PubMed and Scopus databases and Mendeley Catalog (MC)—a publicly accessible bibliographic catalog linked to Elsevier’s Scopus indexing system—covering the period from January 2020 to April 2025. The included studies focused on patients with primary or secondary brain neoplasms and applied machine learning techniques to MRI data for classification or segmentation purposes. Only original research articles written in English and reporting model validation were considered. Studies using animal models, non-imaging data, lacking proper validation, or without accessible full texts (e.g., abstract-only records or publications unavailable through institutional access) were excluded. In total, 108 studies met all inclusion criteria and were analyzed qualitatively. In general, models based on convolutional neural networks (CNNs) were found to dominate current research due to their ability to extract spatial features directly from imaging data. Reported classification accuracies ranged from 95% to 99%, while Dice coefficients for segmentation tasks varied between 0.83 and 0.94. Hybrid architectures (e.g., CNN-SVM, CNN-LSTM) achieved strong results in both classification and segmentation tasks, with accuracies above 95% and Dice scores around 0.90. Transformer-based models, such as the Swin Transformer, reached the highest performance, up to 99.9%. Despite high reported accuracy, challenges remain regarding overfitting, generalization to real-world clinical data, and lack of standardized evaluation protocols. Transfer learning and data augmentation were frequently applied to mitigate limited data availability, while radiomics-based models introduced new avenues for personalized diagnostics. ML has demonstrated substantial potential in enhancing brain MRI analysis and supporting clinical decision-making. Nevertheless, further progress requires rigorous clinical validation, methodological standardization, and comparative benchmarking to bridge the gap between research settings and practical deployment.

This study aimed to systematically review the literature to determine the efficacy and safety of transoral robotic surgery (TORS) in the management of submandibular gland (SMG) sialolithiasis. PubMed, Embase, and Cochrane were searched for English-language articles evaluating TORS in the management of SMG stones published up to 12 September 2022. Nine studies with a total of 99 patients were included. Eight patients underwent TORS followed by sialendoscopy (TS); 11 patients underwent sialendoscopy followed by TORS and sialendoscopy (STS); 4 patients underwent sialendoscopy followed by TORS only (ST); and 4 patients underwent TORS without sialendoscopy (T). The mean operative time amounted to 90.97 min. The mean procedure success rate reached 94.97%, with the highest for ST (100%) and T (100%), followed by the TS (95.04%) and STS (90.91%) variants. The mean follow-up time was 6.81 months. Transient lingual nerve injury occurred in 28 patients (28.3%) and was resolved in all of them within the mean of 1.25 months. No permanent lingual nerve injury was reported. TORS is a safe and effective management modality for hilar and intraparenchymal SMG sialoliths, with high procedural success in terms of successful sialolith removal, SMG preservation, and reduced risk of permanent postoperative lingual nerve damage.

Recently, contrast-enhanced ultrasound (CEUS) has become a promising tool in distinguishing benign from malignant parotid gland tumors. However, its usefulness in differentiating various benign parotid tumors has not been determined so far. This study aimed to systematically review the literature to determine the utility of CEUS in the preoperative differentiation between pleomorphic adenomas (PAs) and Warthin’s tumors (WTs) of the parotid gland. PubMed, Embase, and Cochrane were searched for English-language articles published until 21 July 2022. Fifteen studies were included. On CEUS examination, a significantly greater percentage of PAs displayed heterogeneous enhancement texture compared to WTs. Contrarily, the enhanced lesion size, the enhancement margin, and the presence of the enhancement rim did not differ significantly between the entities. Significantly longer normalized mean transit time (nMTT) and time to peak (TTP) were observed in PAs. Contrarily, the mean values of area under the curve (AUC) and time from peak to one half (TPH) were significantly higher for WTs. Due to the considerable overlap among the qualitative CEUS characteristics of PAs and WTs, the reproducible, investigator-independent quantitative CEUS measurements have a greater potential to distinguish PAs from WTs, which might influence the selection of an appropriate management strategy.