Non-keratinizing carcinoma is the most common subtype of nasopharyngeal carcinoma (NPC). Its poorly differentiated tumor cells and complex microenvironment present challenges to pathological diagnosis. AI-based pathological models have demonstrated p...
The growing adoption of deep learning (DL) in early-stage cancer diagnosis has demonstrated remarkable performance across multiple imaging tasks. Yet, the lack of transparency in these models (“black-box” problem) limits their adoption in...
Semantic segmentation of Signet Ring Cells (SRC) plays a pivotal role in the diagnosis of SRC carcinoma based on pathological images. Deep learning-based methods have demonstrated significant promise in computer-aided diagnosis over the past decade....
29 December 2025
Pathology is fundamental to precision oncology, offering molecular and morphologic insights that enable personalized diagnosis and treatment. Recently, deep learning has demonstrated substantial potential in digital pathology, effectively addressing...
Accurate glomerular segmentation in renal pathological images is a key challenge for chronic kidney disease diagnosis and assessment. Due to the high visual similarity between pathological glomeruli and surrounding tissues in color, texture, and morp...
To quickly and accurately identify the pathological features of the tongue, we developed an intelligent tongue diagnosis system that uses deep learning on a mobile terminal. We also propose an efficient and accurate tongue image processing algorithm...
Deep Gluteal Syndrome (DGS) has traditionally been defined as a clinical entity caused by sciatic nerve (SN) entrapment. However, recent anatomical and imaging studies suggest that muscle- and tendon-origin pathologies—including enthesopathy&md...
Neurological diseases are a significant health threat, often presenting through abnormalities in electroencephalogram (EEG) signals during seizures. In recent years, machine learning (ML) technologies have been explored as a means of automated EEG pa...
Interobserver variations in the pathology of common astrocytic tumors impact diagnosis and subsequent treatment decisions. This study leveraged a residual neural network-50 (ResNet-50) in digital pathological images of diffuse astrocytoma, anaplastic...
We present a Human Artificial Intelligence Hybrid (HAIbrid) integrating framework that reweights Thyroid Imaging Reporting and Data System (TIRADS) features and the malignancy score predicted by a convolutional neural network (CNN) for nodule maligna...
Automated retinal vessel segmentation technology has become an important tool for disease screening and diagnosis in clinical medicine. However, most of the available methods of retinal vessel segmentation still have problems such as poor accuracy an...
Cardiac signals have complex structures representing a combination of simpler structures. In this paper, we develop a new data analytic tool that can extract the complex structures of cardiac signals using the framework of multi-chaotic analysis, whi...
The non-Hodgkin lymphomas (NHLs) are a heterogeneous family of lymphoid malignancies that are among the most common neoplasms of both dogs and humans. Owing to shared molecular, signaling, incidence, and pathologic features, there is a strong framewo...
Glutaric aciduria type 1 (GA-1) is a rare but treatable autosomal-recessive neurometabolic disorder of lysin metabolism caused by biallelic pathogenic variants in glutaryl-CoA dehydrogenase gene (GCDH) that lead to deficiency of GCDH protein. Without...
Lung ultrasound (LUS) has become a crucial part of the investigative tools available in the management of critically ill patients, both within the intensive care unit setting and in prehospital medicine. The increase in its application, in part drive...
Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) are the two most common forms of dementia due to neurodegeneration. AD is characterized by extracellular amyloid-β (Aβ) plaques and intracellular tau neurofibrillary tangles...
This study introduces an innovative deep learning framework to address the limitations of traditional pathological image analysis and the pressing demand for medical resources in tumor diagnosis. With the global rise in cancer cases, manual examinati...
Background and Objectives: Despite ongoing research and evolving diagnostic criteria, progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) remain notoriously difficult to differentiate, largely due to their overlapping clinical presen...
Accurate segmentation and classification of kidney pathologies from medical images remain a major challenge in computer-aided diagnosis due to complex morphological variations, small lesion sizes, and severe class imbalance. This study introduces Dia...
Staining variability in histopathological images compromises automated diagnostic systems by affecting the reliability of computational pathology algorithms. Existing normalization methods prioritize color consistency but often sacrifice critical mor...
In the field of biomedical imaging, the use of Convolutional Neural Networks (CNNs) has achieved impressive success. Additionally, the detection and pathological classification of breast masses creates significant challenges. Traditional mammogram sc...
Nuclei segmentation and classification are two basic and essential tasks in computer-aided diagnosis of digital pathology images, and those deep-learning-based methods have achieved significant success. Unfortunately, most of the existing studies acc...
Colorectal cancer (CRC) is one of the most common causes of cancer-related deaths globally, making a timely and reliable diagnosis essential. Manual histopathology assessment, though clinically standard, is prone to observer variability, while existi...
Membranous nephropathy is one of the most prevalent conditions responsible for nephrotic syndrome in adults. It is clinically nonspecific and mainly diagnosed by kidney biopsy pathology, with three prevalent techniques: light microscopy, electron mic...
Human skeleton data obtained using a depth camera have been used for pathological gait recognition to support doctor or physician diagnosis decisions. Most studies for skeleton-based pathological gait recognition have used either raw skeleton sequenc...
Quantities and diversities of datasets are vital to model training in a variety of medical image diagnosis applications. However, there are the following problems in real scenes: the required data may not be available in a single institution due to t...
Background/Objectives: Oral lesions can present with a wide range of clinical appearances, often making diagnosis challenging, particularly for dental students. This study aimed to identify the most common oral lesions treated at a teaching dental cl...
Atypical parkinsonian disorders—progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and multiple system atrophy (MSA)—are rare, rapidly progressive neurodegenerative syndromes characterized by distinct molecular patholo...
Conventional methods for tumor diagnosis suffer from two inherent limitations: they are time-consuming and subjective. Computer-aided diagnosis (CAD) is an important approach for addressing these limitations. Pathology whole-slide images (WSIs) are h...
Pneumonia remains a serious global health issue, particularly affecting vulnerable groups such as children and the elderly, where timely and accurate diagnosis is critical for effective treatment. Recent advances in deep learning have significantly e...
19 February 2024
Background: The integration of edge computing into smart healthcare systems requires the development of computationally efficient models and methodologies for monitoring and detecting patients’ healthcare statuses. In this context, mobile devic...
Contemporary clinical medicine relies on the integration of clinical observation with physiological and pathological mechanisms to improve diagnosis, therapeutic decision-making, and patient outcomes. However, most current biomedical research interpr...
Mental health disorders are ambiguously defined and diagnosed. The established diagnosis technique, which is based on structured interviews, questionnaires and data subjectively reported by the patients themselves, leaves the mental health field behi...
A vast amount of real-world data, such as pathology reports and clinical notes, are captured as unstructured text in electronic health records (EHRs). However, this information is both difficult and costly to extract through human abstraction, especi...
Chronic kidney disease is a systemic disease of multiple factors and slow progression, and is now becoming a rapidly changing global pathological problem affecting healthcare systems. Anyone who can go through diagnosis before getting to stage 5 Chro...
Mass spectrometry is an effective imaging tool for evaluating biological tissue to detect cancer. With the assistance of deep learning, this technology can be used as a perioperative tissue assessment tool that will facilitate informed surgical decis...
The atopic march encompasses a sequence of allergic conditions, including atopic dermatitis, food allergy, allergic rhinitis, and asthma, that frequently develop in a sequential pattern within the same individual. It was introduced as a conceptual fr...
Precise segmentation of glands in histopathological images is essential for the diagnosis of colorectal cancer, as the changes in gland morphology are associated with pathological progression. Conventional computer-assisted methods rely on dense pixe...
Autism spectrum disorder (ASD) is a combination of developmental anomalies that causes social and behavioral impairments, affecting around 2% of US children. Common symptoms include difficulties in communications, interactions, and behavioral disabil...
In today’s world, heart disease is the leading cause of death globally. Researchers have proposed various methods aimed at improving the accuracy and efficiency of the clinical diagnosis of heart disease. Auxiliary diagnostic systems based on m...
Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, cataly...
Systemic autoimmune rheumatic diseases (SARDs), such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and Sjögren’s syndrome (SS), are traditionally characterized by chronic inflammation and immune-mediated damage to joint...
Metabolic Syndrome (MetS) is a set of risk factors that increase the probability of heart disease or even diabetes mellitus. The diagnosis of the pathology implies compliance with at least three of five risk factors. Doctors obtain two of those facto...
Alzheimer’s disease (AD) is traditionally viewed through the lens of the amyloid cascade hypothesis, implicating amyloid-beta and tau protein aggregates as the main pathological culprits. However, burgeoning research points to the brain’s...
In the past few decades, neuropathology has experienced several paradigm shifts with the introduction of new technologies. Deep learning, a rapidly progressing subfield of machine learning, seems to be the next innovation to alter the diagnostic work...
Tuberculosis remains endemic in the Philippines despite decades of biomedical progress under the WHO End TB Strategy. This persistence reflects not a failure of medicine, but of systems that treat tuberculosis as a biomedical issue rather than a soci...
As the most abundant catecholamine neurotransmitter in the brain, dopamine plays an important role in the normal physiological process, and its level in urine also changes during human pathological processes. In clinic, the detection of dopamine in u...
Thanks to their capability to learn generalizable descriptors directly from images, deep Convolutional Neural Networks (CNNs) seem the ideal solution to most pattern recognition problems. On the other hand, to learn the image representation, CNNs nee...
Background: To design a novel anomaly detection and localization approach using artificial intelligence methods using optical coherence tomography (OCT) scans for retinal diseases. Methods: High-resolution OCT scans from the publicly available Kaggle...
Rapid diagnosis and treatment application in the early stages of H. pylori infection plays an important part in inhibiting the transmission of this infection as this bacterium is involved in various gastric pathologies such as gastritis, gastro-duode...
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