Hydroxyapatite (HAp), a well-known biomaterial, has witnessed a remarkable evolution over the years, transforming from a simple biocompatible substance to an advanced functional material with a wide range of applications. This abstract provides an overview of the significant advancements in the field of HAp and its journey towards becoming a multifunctional material. Initially recognized for its exceptional biocompatibility and bioactivity, HAp gained prominence in the field of bone tissue engineering and dental applications. Its ability to integrate with surrounding tissues, promote cellular adhesion, and facilitate osseointegration made it an ideal candidate for various biomedical implants and coatings. As the understanding of HAp grew, researchers explored its potential beyond traditional biomaterial applications. With advances in material synthesis and engineering, HAp began to exhibit unique properties that extended its utility to other disciplines. Researchers successfully tailored the composition, morphology, and surface characteristics of HAp, leading to enhanced mechanical strength, controlled drug release capabilities, and improved biodegradability. These modifications enabled the utilization of HAp in drug delivery systems, biosensors, tissue engineering scaffolds, and regenerative medicine applications. Moreover, the exceptional biomineralization properties of HAp allowed for the incorporation of functional ions and molecules during synthesis, leading to the development of bioactive coatings and composites with specific therapeutic functionalities. These functionalized HAp materials have demonstrated promising results in antimicrobial coatings, controlled release systems for growth factors and therapeutic agents, and even as catalysts in chemical reactions. In recent years, HAp nanoparticles and nanostructured materials have emerged as a focal point of research due to their unique physicochemical properties and potential for targeted drug delivery, imaging, and theranostic applications. The ability to manipulate the size, shape, and surface chemistry of HAp at the nanoscale has paved the way for innovative approaches in personalized medicine and regenerative therapies. This abstract highlights the exceptional evolution of HAp, from a traditional biomaterial to an advanced functional material. The exploration of novel synthesis methods, surface modifications, and nanoengineering techniques has expanded the horizon of HAp applications, enabling its integration into diverse fields ranging from biomedicine to catalysis. Additionally, this manuscript discusses the emerging prospects of HAp-based materials in photocatalysis, sensing, and energy storage, showcasing its potential as an advanced functional material beyond the realm of biomedical applications. As research in this field progresses, the future holds tremendous potential for HAp-based materials to revolutionize medical treatments and contribute to the advancement of science and technology.

Monitoring the vital signs and physiological responses of the human body in daily activities is particularly useful for the early diagnosis and prevention of cardiovascular diseases. Here, we proposed a wireless and flexible biosensor patch for continuous and longitudinal monitoring of different physiological signals, including body temperature, blood pressure (BP), and electrocardiography. Moreover, these modalities for tracking body movement and GPS locations for emergency rescue have been included in biosensor devices. We optimized the flexible patch design with high mechanical stretchability and compatibility that can provide reliable and long-term attachment to the curved skin surface. Regarding smart healthcare applications, this research presents an Internet of Things-connected healthcare platform consisting of a smartphone application, website service, database server, and mobile gateway. The IoT platform has the potential to reduce the demand for medical resources and enhance the quality of healthcare services. To further address the advances in non-invasive continuous BP monitoring, an optimized deep learning architecture with one-channel electrocardiogram signals is introduced. The performance of the BP estimation model was verified using an independent dataset; this experimental result satisfied the Association for the Advancement of Medical Instrumentation, and the British Hypertension Society standards for BP monitoring devices. The experimental results demonstrated the practical application of the wireless and flexible biosensor patch for continuous physiological signal monitoring with Internet of Medical Things-connected healthcare applications.

Medical image quality assessment (MIQA) is vital in medical imaging and directly affects diagnosis, patient treatment, and general clinical results. Accurate and high-quality imaging is necessary to make accurate diagnoses, efficiently design treatments, and consistently monitor diseases. This review summarizes forty-two research studies on diverse MIQA approaches and their effects on performance in diagnostics, patient results, and efficiency in the process. It contrasts subjective (manual assessment) and objective (rule-driven) evaluation methods, underscores the growing promise of machine intelligence and machine learning (ML) in MIQA automation, and describes the existing MIQA challenges. AI-powered tools are revolutionizing MIQA with automated quality checks, noise reduction, and artifact removal, producing consistent and reliable imaging evaluation. Enhanced image quality is demonstrated in every examination to improve diagnostic precision and support decision making in the clinic. However, challenges still exist, such as variability in quality and variability in human ratings and small datasets hindering standardization. These must be addressed with better-quality data, low-cost labeling, and standardization. Ultimately, this paper reinforces the need for high-quality medical imaging and the potential of MIQA with the power of AI. It is crucial to advance research in this area to advance healthcare.

Speech impairments often emerge as one of the primary indicators of Parkinson’s disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification.

Accurate classification of cancer images plays a crucial role in diagnosis and treatment planning. Deep learning (DL) models have shown promise in achieving high accuracy, but their performance can be influenced by variations in Hematoxylin and Eosin (H&E) staining techniques. In this study, we investigate the impact of H&E stain normalization on the performance of DL models in cancer image classification. We evaluate the performance of VGG19, VGG16, ResNet50, MobileNet, Xception, and InceptionV3 on a dataset of H&E-stained cancer images. Our findings reveal that while VGG16 exhibits strong performance, VGG19 and ResNet50 demonstrate limitations in this context. Notably, stain normalization techniques significantly improve the performance of less complex models such as MobileNet and Xception. These models emerge as competitive alternatives with lower computational complexity and resource requirements and high computational efficiency. The results highlight the importance of optimizing less complex models through stain normalization to achieve accurate and reliable cancer image classification. This research holds tremendous potential for advancing the development of computationally efficient cancer classification systems, ultimately benefiting cancer diagnosis and treatment.

The control of human-machine interfaces (HMIs), such as motorized wheelchairs, has been widely investigated using biopotentials produced by electrochemical processes in the human body. However, many studies in this field sometimes overlook crucial factors like special users' needs, who often have inadequate muscle mass and strength, and paresis needed to operate a wheelchair. This study proposes a novel solution: an economical, universally compatible, and user-centric manual-to-powered wheelchair conversion kit. The powered wheelchair is operated using a hybrid control system integrating electroencephalogram (EEG) and electromyography (EMG), utilizing an LSTM network. It uses a low-cost electroencephalogram (EEG) headset and a wearable electromyography (EMG) electrode armband to solve these constraints. The proposed system comprised three crucial objectives: the development of an EEG-based user attentive detection system, an EMG-based navigation system, and a transform conventional wheelchair into a powered wheelchair. Human test subjects were utilized to evaluate the proposed system, and the study complied with accepted ethical guidelines. We selected four EEG features (p < 0.023) for the attentive detection system and six EMG features (p < 0.037) to detect navigation intentions. User attentive detection was achieved at 83.33 (±0.34) %, while the navigation intention system produced 86.67 (±0.52) % accuracy. The overall system was successful in reaching an accuracy rate of 85.0 (±0.19) % and a weighted average precision of 0.89. After the dataset was trained using an LSTM network, the overall accuracy produced was 97.3 (±0.5) %, higher than the accuracy produced by the Quadratic SVM classifier. By giving older and disabled people a more convenient way to use powered wheelchairs, this research helps to build ergonomic and cost-effective biopotential-based HMIs, enhancing their quality of life.