Search Results (450)

The diagnosis of depression is a critical topic in the medical field. For years, the electroencephalogram (EEG) has been considered an objective and cost-effective detection tool. However, most studies on depression recognition models tend to extract information solely from the original temporal scale of EEG signals, ignoring the usage of coarse scales. This study aims to explore the feasibility of multiscale analysis for a depression recognition model and to research its temporal characteristics. Based on two types of multiscale entropy, this paper constructs a machine learning model using classifiers including LDA, LR, RBF-SVM, and KNN. The relation between the temporal scale and model performance was examined through mathematical analysis. The experimental results showed that the highest classification accuracy achieved was 96.42% with KNN at scale 3. Among various classifiers, scales 3 and 9 outperformed other scales. The model performance is correlated with the scale variation. Within a finite range, an optimal scale likely exists. The algorithm complexity is linearly related to the temporal scale. By accepting predictable computational costs, a stable improvement in model performance can be achieved. This multiscale analysis is practical in building and optimizing a depression recognition model. Further investigation of the relation between the temporal scale and model capabilities could advance the application of computer-assisted diagnosis. Full article

The most deadly type of skin cancer is melanoma. A visual examination does not provide an accurate diagnosis of melanoma during its early to middle stages. Therefore, an automated model could be developed that assists with early skin cancer detection. It is possible to limit the severity of melanoma by detecting it early and treating it promptly. This study aims to develop efficient approaches for various phases of melanoma computer-aided diagnosis (CAD), such as preprocessing, segmentation, and classification. The first step of the CAD pipeline includes the proposed hybrid method, which uses morphological operations and context aggregation-based deep neural networks to remove hairlines and improve poor contrast in dermoscopic skin cancer images. An image segmentation network based on deep learning is then used to extract lesion regions for detailed analysis and calculate the optimized classification features. Lastly, a deep neural network is used to distinguish melanoma from benign lesions. The proposed approaches use a benchmark dataset named International Skin Imaging Collaboration (ISIC) 2020. In this work, two forms of evaluations are performed with the classification model. The first experiment involves the incorporation of the results from the preprocessing and segmentation stages into the classification model. The second experiment involves the evaluation of the classifier without employing these stages i.e., using raw images. From the study results, it can be concluded that a classification model using segmented and cleaned images contributes more to achieving an accurate classification rate of 93.40% with a 1.3 s test time on a single image. Full article

Edge detection methods are significant in medical imaging-assisted diagnosis. However, existing methods based on grayscale gradient computation still need to be optimized in practicality, especially in terms of actual visual quality and sensitivity to image contrast. To optimize the visualization and enhance the robustness of contrast changes, we propose the Contrast Invariant Edge Detection (CIED) method. CIED combines Gaussian filtering and morphological processing methods to preprocess medical images. It utilizes the three Most Significant Bit (MSB) planes and binary images to detect and extract significant edge information. Each bit plane is used to detect edges in 3 × 3 blocks by the proposed algorithm, and then the edge information from each plane is fused to obtain an edge image. This method is generalized to common types of images. Since CIED is based on binary bit planes and eliminates complex pixel operations, it is faster and more efficient. In addition, CIED is insensitive to changes in image contrast, making it more flexible in its application. To comprehensively evaluate the performance of CIED, we develop a medical image dataset and conduct edge image and contrast evaluation experiments based on these images. The results show that the average precision of CIED is 0.408, the average recall is 0.917, and the average F1-score is 0.550. The results indicate that CIED is not only more practical in terms of visual effects but also robust in terms of contrast invariance. The comparison results with other methods also confirm the advantages of CIED. This study provides a novel approach for edge detection within medical images. Full article

Background: Artificial intelligence (AI) has recently made unprecedented contributions in every walk of life, but it has not been able to work its way into diagnostic medicine and standard clinical practice yet. Although data scientists, researchers, and medical experts have been working in the direction of designing and developing computer aided diagnosis (CAD) tools to serve as assistants to doctors, their large-scale adoption and integration into the healthcare system still seems far-fetched. Diagnostic radiology is no exception. Imagining techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans have been widely and very effectively employed by radiologists and neurologists for the differential diagnoses of neurological disorders for decades, yet no AI-powered systems to analyze such scans have been incorporated into the standard operating procedures of healthcare systems. Why? It is absolutely understandable that in diagnostic medicine, precious human lives are on the line, and hence there is no room even for the tiniest of mistakes. Nevertheless, with the advent of explainable artificial intelligence (XAI), the old-school black boxes of deep learning (DL) systems have been unraveled. Would XAI be the turning point for medical experts to finally embrace AI in diagnostic radiology? This review is a humble endeavor to find the answers to these questions. Methods: In this review, we present the journey and contributions of AI in developing systems to recognize, preprocess, and analyze brain MRI scans for differential diagnoses of various neurological disorders, with special emphasis on CAD systems embedded with explainability. A comprehensive review of the literature from 2017 to 2024 was conducted using host databases. We also present medical domain experts’ opinions and summarize the challenges up ahead that need to be addressed in order to fully exploit the tremendous potential of XAI in its application to medical diagnostics and serve humanity. Results: Forty-seven studies were summarized and tabulated with information about the XAI technology and datasets employed, along with performance accuracies. The strengths and weaknesses of the studies have also been discussed. In addition, the opinions of seven medical experts from around the world have been presented to guide engineers and data scientists in developing such CAD tools. Conclusions: Current CAD research was observed to be focused on the enhancement of the performance accuracies of the DL regimens, with less attention being paid to the authenticity and usefulness of explanations. A shortage of ground truth data for explainability was also observed. Visual explanation methods were found to dominate; however, they might not be enough, and more thorough and human professor-like explanations would be required to build the trust of healthcare professionals. Special attention to these factors along with the legal, ethical, safety, and security issues can bridge the current gap between XAI and routine clinical practice. Full article

Background: Alzheimer’s disease is a progressive neurological condition marked by a decline in cognitive abilities. Early diagnosis is crucial but challenging due to overlapping symptoms among impairment stages, necessitating non-invasive, reliable diagnostic tools. Methods: We applied information geometry and manifold learning to analyze grayscale MRI scans classified into No Impairment, Very Mild, Mild, and Moderate Impairment. Preprocessed images were reduced via Principal Component Analysis (retaining 95% variance) and converted into statistical manifolds using estimated mean vectors and covariance matrices. Geodesic distances, computed with the Fisher Information metric, quantified class differences. Graph Neural Networks, including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and GraphSAGE, were utilized to categorize impairment levels using graph-based representations of the MRI data. Results: Significant differences in covariance structures were observed, with increased variability and stronger feature correlations at higher impairment levels. Geodesic distances between No Impairment and Mild Impairment (58.68, $p<0.001$) and between Mild and Moderate Impairment (58.28, $p<0.001$) are statistically significant. GCN and GraphSAGE achieve perfect classification accuracy (precision, recall, F1-Score: 1.0), correctly identifying all instances across classes. GAT attains an overall accuracy of 59.61%, with variable performance across classes. Conclusions: Integrating information geometry, manifold learning, and GNNs effectively differentiates AD impairment stages from MRI data. The strong performance of GCN and GraphSAGE indicates their potential to assist clinicians in the early identification and tracking of Alzheimer’s disease progression. Full article

Background: Screening for cardiovascular disease (CVD) and its associated risk factors in childhood facilitates early detection and timely preventive interventions. However, limited data are available regarding screening tools and their diagnostic yield when applied in unselected pediatric populations. Aims: To evaluate the performance of a CVD screening program, based on history, 12-lead ECG and phonocardiography, applied in primary school children. Methods: The methods used were prospective study, with voluntary participation of third-grade primary school children in the region of Crete/Greece, over 6 years (2018–2024). Personal and family history were collected by using a standardized questionnaire and physical evaluation (including weight, height, blood pressure measurement), and cardiac auscultation (digital phonocardiography (PCG)) and 12-lead electrocardiogram (ECG) were recorded at local health stations (Phase I). Following expert verification of responses and obtained data, assisted by designated electronic health record with incorporated decision support algorithms (phase II), pediatric cardiology evaluation at the tertiary referral center followed (phase III). Results: A total of 944 children participated (boys 49.6%). A total of 790 (83.7%) had Phase I referral indication, confirmed in 311(32.9%) during Phase II evaluation. Adiposity (10.8%) and hypertension (3.2%) as risk factors for CVD were documented in 10.8% and 3.2% of the total population, respectively. During Phase III evaluations (n = 201), the majority (n = 132, 14% of total) of children were considered as having a further indication for evaluation by other pediatric subspecialties for their reported symptoms. Abnormal CVD findings were present in 69 (7.3%) of the study population, including minor/trivial structural heart disease in 23 (2.4%) and 17 (1.8%), respectively, referred due to abnormal cardiac auscultation, and ECG abnormalities in 29 (3%), of which 6 (0.6%) were considered potentially significant (including 1 case of genetically confirmed channelopathy-LQT syndrome). Conclusions: CVD screening programs in school children can be very helpful for the early detection of CVD risk factors and of their general health as well. Expert cardiac auscultation and 12-lead ECG allow for the detection of structural and arrhythmogenic heard disease, respectively. Further study is needed regarding performance of individual components, accuracy of interpretation (including computer assisted diagnosis) and cost-effectiveness, before large-scale application of CVD screening in unselected pediatric populations. Full article

Objective: We aim to construct an artificial intelligence (AI)-assisted nasal endoscopy diagnostic system capable of preliminary differentiation and identification of nasal neoplasia properties, as well as intraoperative tracking, providing an important basis for nasal endoscopic surgery. Methods: We retrospectively analyzed 1050 video data of nasal endoscopic surgeries involving four types of nasal neoplasms. Using Deep Snake, U-Net, and Att-Res2-UNet, we developed a nasal neoplastic detection network based on endoscopic images. After deep learning, the optimal network was selected as the initialization model and trained to optimize the SiamMask online tracking algorithm. Results: The Att-Res2-UNet network demonstrated the highest accuracy and precision, with the most accurate recognition results. The overall accuracy of the model established by us achieved an overall accuracy similar to that of residents (0.9707 ± 0.00984), while slightly lower than that of rhinologists (0.9790 ± 0.00348). SiamMask’s segmentation range was consistent with rhinologists, with a 99% compliance rate and a neoplasm probability value ≥ 0.5. Conclusions: This study successfully established an AI-assisted nasal endoscopic diagnostic system that can preliminarily identify nasal neoplasms from endoscopic images and automatically track them in real time during surgery, enhancing the efficiency of endoscopic diagnosis and surgery. Full article

Various imaging methods assist in diagnosing periprosthetic joint infection (PJI). These include radiological techniques such as radiography, computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US); as well as advanced nuclear medicine techniques including bone scintigraphy (BS), anti-granulocyte antibody imaging (AGS), leukocyte scintigraphy (LS), and fluorodeoxyglucose positron emission tomography (FDG-PET and FDG-PET/CT). Each imaging technique and radiopharmaceutical has been extensively studied, with unique diagnostic accuracy, limitations, and benefits for PJI diagnosis. This review aims to detail and describe the most commonly used imaging techniques and radiopharmaceuticals for evaluating PJI, focusing particularly on knee and hip arthroplasties. Full article

On 11 February 2020, the prevalent outbreak of COVID-19, a coronavirus illness, was declared a global pandemic. Since then, nearly seven million people have died and over 765 million confirmed cases of COVID-19 have been reported. The goal of this study is to develop a diagnostic tool for detecting COVID-19 infections more efficiently. Currently, the most widely used method is Reverse Transcription Polymerase Chain Reaction (RT-PCR), a clinical technique for infection identification. However, RT-PCR is expensive, has limited sensitivity, and requires specialized medical expertise. One of the major challenges in the rapid diagnosis of COVID-19 is the need for reliable imaging, particularly X-ray imaging. This work takes advantage of artificial intelligence (AI) techniques to enhance diagnostic accuracy by automating the detection of COVID-19 infections from chest X-ray (CXR) images. We obtained and analyzed CXR images from the Kaggle public database (4035 images in total), including cases of COVID-19, viral pneumonia, pulmonary opacity, and healthy controls. By integrating advanced techniques with transfer learning from pre-trained convolutional neural networks (CNNs), specifically InceptionV3, ResNet50, and Xception, we achieved an accuracy of 95%, significantly higher than the 85.5% achieved with ResNet50 alone. Additionally, our proposed method, CXR-DNNs, can accurately distinguish between three different types of chest X-ray images for the first time. This computer-assisted diagnostic tool has the potential to significantly enhance the speed and accuracy of COVID-19 diagnoses. Full article

Background/Objectives: Pediatric facial fractures present unique challenges due to the anatomical, physiological, and developmental differences in children’s facial structures. The growing facial bones in children complicate diagnosis and treatment. This review explores the advancements and complexities in managing pediatric facial fractures, focusing on innovations in diagnosis, treatment strategies, and multidisciplinary care. Methods: A narrative review was conducted, synthesizing data from English-language articles published between 2001 and 2024. Relevant studies were identified through databases such as PubMed, Scopus, Lilacs, Embase, and SciELO using keywords related to pediatric facial fractures. This narrative review focuses on anatomical challenges, advancements in diagnostic techniques, treatment approaches, and the role of interdisciplinary teams in management. Results: Key findings highlight advancements in imaging technologies, including three-dimensional computed tomography (3D CT) and magnetic resonance imaging (MRI), which have improved fracture diagnosis and preoperative planning. Minimally invasive techniques and bioresorbable implants have revolutionized treatment, reducing trauma and enhancing recovery. The integration of multidisciplinary teams, including pediatricians, psychologists, and speech therapists, has become crucial in addressing both the physical and emotional needs of patients. Emerging technologies such as 3D printing and computer-assisted navigation are shaping future treatment approaches. Conclusions: The management of pediatric facial fractures has significantly advanced due to innovations in imaging, surgical techniques, and the growing importance of interdisciplinary care. Despite these improvements, long-term follow-up remains critical to monitor potential complications. Ongoing research and collaboration are essential to refine treatment strategies and improve long-term outcomes for pediatric patients with facial trauma. Full article

Artificial intelligence (AI) is an area of computer science that focuses on designing machines or systems that can perform operations that would typically need human intelligence. AI is a rapidly developing technology that has grabbed the interest of researchers from all across the globe in the healthcare industry. Advancements in machine learning and data analysis have revolutionized oral health diagnosis, treatment, and management, making it a transformative force in healthcare, particularly in dentistry. Particularly in dentistry, AI is becoming increasingly prevalent as it contributes to the diagnosis of oro-facial diseases, offers treatment modalities, and manages practice in the dental operatory. All dental disciplines, including oral medicine, operative dentistry, pediatric dentistry, periodontology, orthodontics, oral and maxillofacial surgery, prosthodontics, and forensic odontology, have adopted AI. The majority of AI applications in dentistry are for diagnoses based on radiographic or optical images, while other tasks are less applicable due to constraints such as data availability, uniformity, and computational power. Evidence-based dentistry is considered the gold standard for decision making by dental professionals, while AI machine learning models learn from human expertise. Dentistry AI and technology systems can provide numerous benefits, such as improved diagnosis accuracy and increased administrative task efficiency. Dental practices are already implementing various AI applications, such as imaging and diagnosis, treatment planning, robotics and automation, augmented and virtual reality, data analysis and predictive analytics, and administrative support. The dentistry field has extensively used artificial intelligence to assist less-skilled practitioners in reaching a more precise diagnosis. These AI models effectively recognize and classify patients with various oro-facial problems into different risk categories, both individually and on a group basis. The objective of this descriptive review is to review the most recent developments of AI in the field of dentistry. Full article

A battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients’ health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for electrocardiogram (ECG) signal processing and personalized health monitoring. The developed electronic module provides a customizable approach to power the device using a lithium-ion battery, a series of silicon photodiode arrays, and a solar panel. The new architecture and techniques offered by the developed method include an analog front-end unit, a signal processing unit, and a battery management unit for the acquiring and processing of real-time ECG signals. The dynamic multi-level wavelet packet decomposition framework has been used and applied to an ECG signal to extract the desired features by removing overlapped and repeated samples from an ECG signal. Further, a random forest with deep decision tree (RFDDT) architecture has been designed for offline ECG signal classification, and experimental results provide the highest accuracy of 99.72%. One assesses the custom-developed sensor by comparing its data with those of conventional biosensors. The onboard energy-harvesting and battery management circuits are designed with a BQ25505 microprocessor with the support of silicon photodiodes and solar cells which detect the ambient light variations and provide a maximum of 4.2 V supply to enable the continuous operation of an entire module. The measurements conducted on each unit of the proposed method demonstrate that the proposed signal-processing method significantly reduces the overlapping samples from the raw ECG data and the timing requirement criteria for personalized and wearable health monitoring. Also, it improves temporal requirements for ECG data processing while achieving excellent classification performance at a low computing cost. Full article

Background: Nasal and paranasal sinus masses can arise from a wide range of conditions, both benign and malignant, as well as congenital or acquired. Diagnosing these masses is often challenging, requiring a combination of nasal endoscopy, imaging studies, and histopathological analysis. Initial imaging frequently involves computed tomography or cone beam computed tomography (CBCT) to evaluate the bony anatomy of the nasal cavity and surrounding sinuses, while magnetic resonance imaging (MRI) is typically used for detailed assessment of soft tissues and to aid in differential diagnosis when the findings are inconclusive. Methods: This review examines nasal masses evaluated using CT, CBCT, and MRI, highlighting key imaging features that may assist in differential diagnosis. Results: For non-neoplastic lesions, examples include conditions such as rhinoliths, inverted mesiodens, and septal mucoceles. Benign and borderline tumors discussed encompass lobular capillary hemangioma, inverted papilloma, septal osteoma, chondromesenchymal hamartoma, hemangioma, hemangiopericytoma, antrochoanal polyp, sinonasal angiofibroma, ossifying fibroma, and lipoma. Malignant tumors addressed in this review include adenocarcinoma, esthesioneuroblastoma, non-Hodgkin lymphoma, melanoma, and sarcoma. Conclusions: Diagnosing nasal lesions represent a significant challenge for otolaryngologists. Imaging characteristics of nasal masses play a crucial role in narrowing down differential diagnoses before surgery. However, nasal endoscopy combined with biopsy remains the definitive diagnostic approach. Full article

Three-dimensional (3D) printing is an advanced technology for accurately understanding anatomy and supporting the successful surgical management of complex congenital heart disease (CHD). We aimed to evaluate whether our super-flexible 3D heart models could facilitate preoperative decision-making and surgical simulation for complex CHD. The super-flexible heart models were fabricated by stereolithography 3D printing of the internal and external contours of the heart from cardiac computed tomography (CT) data, followed by vacuum casting with a polyurethane material similar in elasticity to a child’s heart. Nineteen pediatric patients with complex CHD were enrolled (median age, 10 months). The primary endpoint was defined as the percentage of patients rated as “essential” on the surgeons’ postoperative 5-point Likert scale. The accuracy of the models was validated by a non-destructive method using industrial CT. The super-flexible heart models allowed detailed anatomical diagnosis and simulated surgery with incisions and sutures. Thirteen patients (68.4%) were classified as “essential” by the primary surgeons after surgery, with a 95% confidence interval of 43.4–87.4%, meeting the primary endpoint. The product error within 90% of the total external and internal surfaces was 0.54 ± 0.21 mm. The super-flexible 3D heart models are accurate, reliable, and useful tools to assist surgeons in decision-making and allow for preoperative simulation in CHD. Full article

Artificial intelligence (AI), specifically Generative Pre-trained Transformer (GPT) technology, has revolutionized various fields, including medicine and dentistry. The AI model ChatGPT, developed by OpenAI, mimics human language on a large scale, generating coherent and contextually appropriate responses. ChatGPT serves as an auxiliary resource for diagnosis and decision-making across various medical disciplines. This comprehensive narrative review aims to explore how ChatGPT can assist the dental sector, highlighting its potential to enhance various aspects of the discipline. This review includes a literature search on the application of ChatGPT in dentistry, with a focus on the differences between the free version, ChatGPT 3.5, and the more advanced subscription-based version, ChatGPT 4. Specifically, ChatGPT has proven to be effective in enhancing user interaction, providing fast and accurate information and improving the accessibility of knowledge. However, despite these advantages, several limitations are identified, including concerns regarding the accuracy of responses in complex scenarios, ethical considerations surrounding its use, and the need for improved training to handle highly specialized queries. In conclusion, while ChatGPT offers numerous benefits in terms of efficiency and scalability, further research and development are needed to address these limitations, particularly in areas requiring greater precision, ethical oversight, and specialized expertise. Full article