The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), machine learning (ML), supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) have significantly transformed the field of biomaterials. These technologies have introduced new possibilities for the design, optimization, and predictive modeling of biomaterials. This review explores the applications of DL and AI in biomaterial development, emphasizing their roles in optimizing material properties, advancing innovative design processes, and accurately predicting material behaviors. We examine the integration of DL in enhancing the performance and functional attributes of biomaterials, explore AI-driven methodologies for the creation of novel biomaterials, and assess the capabilities of ML in predicting biomaterial responses to various environmental stimuli. Our aim is to elucidate the pivotal contributions of DL, AI, and ML to biomaterials science and their potential to drive the innovation and development of superior biomaterials. It is suggested that future research should further deepen these technologies’ contributions to biomaterials science and explore new application areas.

The task of semantic segmentation holds a fundamental position in the field of computer vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent times, significant advancements have been achieved in the field of semantic segmentation through the application of Convolutional Neural Networks (CNN) techniques based on deep learning. This paper presents a comprehensive and structured analysis of approximately 150 methods of semantic segmentation based on CNN within the last decade. Moreover, it examines 15 well-known datasets in the semantic segmentation field. These datasets consist of 2D and 3D image and video frames, including general, indoor, outdoor, and street scenes. Furthermore, this paper mentions several recent techniques, such as SAM, UDA, and common post-processing algorithms, such as CRF and MRF. Additionally, this paper analyzes the performance evaluation of reviewed state-of-the-art methods, pioneering methods, common backbone networks, and popular datasets. These have been compared according to the results of Mean Intersection over Union (MIoU), the most popular evaluation metric of semantic segmentation. Finally, it discusses the main challenges and possible solutions and underlines some future research directions in the semantic segmentation task. We hope that our survey article will be useful to provide a foreknowledge to the readers who will work in this field.

Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019–May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.

Purpose: Parkinson’s Disease comes on top among neurodegenerative diseases affecting 10 million worldwide. To detect Parkinson’s Disease in a prior state, gait analysis is an effective choice. However, monitoring of Parkinson’s Disease using gait analysis is time consuming and exhaustive for patients and physicians. To assess severity of symptoms, a rating scale called Unified Parkinson's Disease Rating Scale is used. It determines mild and severe cases. Today, Parkinson’s Disease severity assessment is made in gait laboratories and by manual examination. These are time consuming and it is costly for health institutions to build and maintain laboratories. By using low-cost wearables and an effective model, aforementioned problems can be solved. Methods: We provide a computerized solution for quantifiable assessment of Parkinson’s Disease symptoms severity. By using wearable sensors, our framework can predict exact symptom values to assess Parkinson’s Disease severity. We propose a deep learning approach that utilizes Ground Reaction Force sensors. From sensor signals, features are extracted and fed to a hybrid deep learning model. This model is the combination of Convolutional Neural Networks and Locally Weighted Random Forest. Results: Proposed framework achieved 0.897, 3.009, 4.556 in terms of Correlation Coefficient, Mean Absolute Error and Root Mean Square Error, respectively. Proposed framework outperformed other machine and deep learning models. We also evaluated classification performance for disease detection. We outperformed most of the previous studies, achieving 99.5% accuracy, 98.7% sensitivity and 99.1% specificity. Conclusion: This is the first study to use a deep learning regression approach to predict exact symptom value of Parkinson’s Disease patients. Results show that this approach can be effectively employed as a disease severity assessment tool using wearable sensors.

The integration of artificial intelligence (AI) in wastewater treatment management offers a promising approach to optimizing effluent quality predictions and enhancing operational efficiency. This study evaluates the performance of machine learning models in predicting key wastewater effluent parameters Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Suspended Solids (TSS), Total Effluent Nitrogen and Total Effluent Phosphorus. Three feature selection techniques were applied: SelectKBest, Mutual Information, and Recursive Feature Elimination (RFE) using Random Forest to identify the most significant predictors. The study leveraged ensemble learning models, including XGBoost, Random Forest, Gradient Boosting, and LightGBM, and compared them with Decision Tree models. The results demonstrate that effluent volatile suspended solids (VSS) consistently held the highest predictive importance across all feature selection methods. Ensemble models significantly outperformed Decision Trees, with Gradient Boosting achieving the best predictive accuracy for TSS and total nitrogen (Mean Absolute Error (MAE): 3.667 $$R^2$$ : 97.53), XGBoost excelling in COD prediction with MAE and $$R^2$$ of 6.251 and 83. 41%, respectively, and XGBoost showing superior performance for BOD (MAE: 1.589 $$R^2$$ :79.64%). LightGBM yielded the highest precision in predicting total phosphate with MAE and a $$R^2$$ score of 0.230 and 28. 68%, respectively. Decision tree models consistently underperformed, exhibiting the highest error rates. These findings highlight the potential of AI-driven approaches in wastewater management to improve decision-making, regulatory compliance, and resource efficiency. However, limitations such as operational irregularities and seasonal variations remain challenges for further refinement.