Melanoma is a highly aggressive skin cancer, where early and accurate diagnosis is crucial to improve patient outcomes. Dermoscopy, a non-invasive imaging technique, aids in melanoma detection but can be limited by subjective interpretation. Recently, machine learning and deep learning techniques have shown promise in enhancing diagnostic precision by automating the analysis of dermoscopy images. This systematic review examines recent advancements in machine learning (ML) and deep learning (DL) applications for melanoma diagnosis and prognosis using dermoscopy images. We conducted a thorough search across multiple databases, ultimately reviewing 34 studies published between 2016 and 2024. The review covers a range of model architectures, including DenseNet and ResNet, and discusses datasets, methodologies, and evaluation metrics used to validate model performance. Our results highlight that certain deep learning architectures, such as DenseNet and DCNN demonstrated outstanding performance, achieving over 95% accuracy on the HAM10000, ISIC and other datasets for melanoma detection from dermoscopy images. The review provides insights into the strengths, limitations, and future research directions of machine learning and deep learning methods in melanoma diagnosis and prognosis. It emphasizes the challenges related to data diversity, model interpretability, and computational resource requirements. This review underscores the potential of machine learning and deep learning methods to transform melanoma diagnosis through improved diagnostic accuracy and efficiency. Future research should focus on creating accessible, large datasets and enhancing model interpretability to increase clinical applicability. By addressing these areas, machine learning and deep learning models could play a central role in advancing melanoma diagnosis and patient care.

Glioblastoma Multiforme (GBM), classified as a grade IV glioma by the World Health Organization (WHO), is a prevalent and notably aggressive form of brain tumor derived from glial cells. It stands as one of the most severe forms of primary brain cancer in humans. The median survival time of GBM patients is only 12–15 months, making it the most lethal type of brain tumor. Every year, about 200,000 people worldwide succumb to this disease. GBM is also highly heterogeneous, meaning that its characteristics and behavior vary widely among different patients. This leads to different outcomes and survival times for each individual. Predicting the survival of GBM patients accurately can have multiple benefits. It can enable optimal and personalized treatment planning based on the patient's condition and prognosis. It can also support the patients and their families to cope with the possible outcomes and make informed decisions about their care and quality of life. Furthermore, it can assist the researchers and scientists to discover the most relevant biomarkers, features, and mechanisms of the disease and to design more effective and personalized therapies. Artificial intelligence methods, such as machine learning and deep learning, have been widely applied to survival prediction in various fields, such as breast cancer, lung cancer, gastric cancer, cervical cancer, liver cancer, prostate cancer, and covid 19. This systematic review summarizes the current state-of-the-art methods for predicting glioblastoma survival using different types of input data, such as clinical features, molecular markers, imaging features, radiomics features, omics data or a combination of them. Following PRISMA guidelines, we searched databases from 2015 to 2024, reviewing 107 articles meeting our criteria. We analyzed the data sources, methods, performance metrics and outcomes of the studies. We found that random forest was the most popular method, and a combination of radiomics and clinical data was the most common input data.

Background: Dental caries, also known as tooth decay, is a widespread and long-standing condition that affects people of all ages. This ailment is caused by bacteria that attach themselves to teeth and break down sugars, creating acid that gradually wears away at the tooth structure. Tooth discoloration, pain, and sensitivity to hot or cold foods and drinks are common symptoms of tooth decay. Although this condition is prevalent among all age groups, it is especially prevalent in children with baby teeth. Early diagnosis of dental caries is critical to preventing further decay and avoiding costly tooth repairs. Currently, dentists employ a time-consuming and repetitive process of manually marking tooth lesions after conducting radiographic exams. However, with the rapid development of artificial intelligence in medical imaging research, there is a chance to improve the accuracy and efficiency of dental diagnosis. Methods: This study introduces a data-driven model for accurately diagnosing dental decay through the use of Bitewing radiology images using convolutional neural networks. The dataset utilized in this research includes 713 patient images obtained from the Samin Maxillofacial Radiology Center located in Tehran, Iran. The images were captured between June 2020 and January 2022 and underwent processing via four distinct Convolutional Neural Networks. The images were resized to 100 × 100 and then divided into two groups: 70% (4219) for training and 30% (1813) for testing. The four networks employed in this study were AlexNet, ResNet50, VGG16, and VGG19. Results: Among different well-known CNN architectures compared in this study, the VGG19 model was found to be the most accurate, with a 93.93% accuracy. Conclusion: This promising result indicates the potential for developing an automatic AI-based dental caries diagnostic model from Bitewing images. It has the potential to serve patients or dentists as a mobile app or cloud-based diagnosis service (clinical decision support system).

Nowadays, data are generated in a continuous streaming manner as the inputs of various applications. The sources of such generated data can be wired or wireless sensor networks commonly used in various fields of geographical, traffic, Internet of Things (IoT), financial tickers, Web2 and Web3, e-commerce, social networks, and online communities. The high volume, high variety, and high velocity of data have recently posed the challenge of 3Vs to this field, also known as the Big Data Problem. The 3Vs dimensions of complexities for the big data entails high-speed storage, scalability of database systems, suitable data models, real-time responsiveness and so on. Data model, as the representation schema of data is an essential issue since many others (e.g., DBMS systems’ design, DB languages, etc.) rely on. So, the study of data models is a key and fundamental aspect in structuring, organizing, storing, and manipulating big data. It is also the essence in various areas of cloud migration, web-scale, and so forth. In this paper, we have systematically reviewed different types of data models, the rationale behind them, their applications and support capabilities, and the technologies to switch from one model to another. To address the user needs in various fields, a systematic review method is adopted to classify and present different types and characteristics of data models.