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Deciphering Wrist Pain: A Comprehensive MRI-Based Classification and Interpretation Approach.

Wrist pain is a frequent clinical challenge with diverse etiologies requiring precise imaging assessment. Magnetic resonance imaging is the gold standard for evaluating soft tissue and osseous pathology, facilitating accurate classification and diagnosis. This review presents a structured magnetic resonance imaging-based framework for interpreting wrist pain, emphasizing clinically relevant findings in triangular fibrocartilage complex injuries, interosseous ligament pathology, ulnocarpal impaction, and midcarpal instability. By integrating anatomical insights with imaging characteristics, thi…

[Clinical application and Zunyi Classification of free anterolateral thigh chimeric flaps].

Objective: To summarize the clinical application and Zunyi Classification of free anterolateral thigh chimeric flaps. Methods: This study was a retrospective observational study. From June 2021 to June 2024, 200 patients who underwent free anterolateral thigh chimeric flap transplantation and met the inclusion criteria were admitted to the Affiliated Hospital of Zunyi Medical University, including 106 males and 94 females, aged 3 to 77 years. The wound types included mechanical trauma wounds in 175 cases, chronic wounds in 18 cases, and other wounds (postoperative infection, burns, etc.) in 7 …

Co-secreting pituitary adenomas in acromegaly: Classification, diagnosis, and treatment strategies.

Co-secreting pituitary adenomas (PAs) represent a subset of pituitary tumors with distinct clinical and pathological characteristics. These tumors can secrete both growth hormone (GH) and prolactin (PRL), impacting the clinical presentation, treatment response, and long-term outcomes of acromegaly. This chapter reviews the classification, pathophysiology, and clinical implications of GH and PRL co-secreting PAs, integrating data from previously published studies. Additionally, we discuss treatment strategies, including surgical and medical approaches, highlighting the impact of the treatment w…

Clinical-pathologic classification of anti-HMGCR-positive immune-mediated necrotizing myopathy.

Anti-HMGCR-positive immune-mediated necrotizing myopathy (IMNM), also anti-HMGCR myopathy, was initially considered as an exclusively skeletal muscular disease, observed in elderly patients with an acute onset. However, there is an increasing number of patients presenting greater clinical diversity than previously recognized. The aim of this study is to establish a comprehensive clinical-pathological classification for these patients, capturing the spectrum of disease presentations including both typical and atypical cases. We conducted a retrospective analysis and follow-up study of the clini…

Multi-modal convolutional neural network-based thyroid cytology classification and diagnosis.

BACKGROUND: The cytologic diagnosis of thyroid nodules' benign and malignant nature based on cytological smears obtained through ultrasound-guided fine-needle aspiration is crucial for determining subsequent treatment plans. The development of artificial intelligence (AI) can assist pathologists in improving the efficiency and accuracy of cytological diagnoses. We propose a novel diagnostic model based on a network architecture that integrates cytologic images and digital ultrasound image features (CI-DUF) to solve the multi-class classification task of thyroid fine-needle aspiration cytology.…

Cytological Classification Diagnosis for Thyroid Nodules via Multimodal Model Deep Learning.

The rising prevalence of thyroid nodules is straining limited cytopathology resources, resulting in excessive overdiagnosis and overtreatment with significant patient and healthcare consequences. To address this, AI-TFNA is developed, a robust artificial intelligence platform leveraging extensive clinical data to enhance diagnostic accuracy and clinical efficiency. A total of 20,803 thyroid samples are collected from seven medical centers across different regions in China. Of these, 4,421 thyroid fine-needle aspiration (TFNA) samples from three hospitals are used to train AI-TFNA, ensuring str

Subtype classification of gastric spindle cell tumors in whole slide images.

AIMS: Accurate cancer subtype classification is critical due to variations in tumor progression and prognosis. Traditionally, pathologists classified subtypes manually by examining pathological slides under the microscope. To address increasing workloads, this study aimed to classify gastric spindle cell tumor (GSCT) subtypes-gastrointestinal stromal tumors (GIST), leiomyomas, and schwannomas-using a convoluted neural network applied to whole-slide images (WSIs). To date, no automated pipeline has been proposed for classifying GSCTs into GIST, leiomyoma, and schwannoma from hematoxylin and eos…

Generalizable deep neural networks for image quality classification of cervical images.

Successful translation of artificial intelligence (AI) models into clinical practice, across clinical domains, is frequently hindered by the lack of image quality control. Diagnostic models are often trained on images with no denotation of image quality in the training data; this, in turn, can lead to misclassifications by these models when implemented in the clinical setting. In the case of cervical images, quality classification is a crucial task to ensure accurate detection of precancerous lesions or cancer; this is true for both gynecologic-oncologists' (manual) and diagnostic AI models' (…

Recent Advances in Pineoblastoma Research: Molecular Classification, Modelling and Targetable Vulnerabilities.

Pineoblastoma (PB) is a rare yet lethal pediatric brain cancer of the pineal gland, a small endocrine organ that secretes melatonin to regulate the circadian rhythm. For PB patients ≤5 years of age, the overall survival rate is approximately 15%; metastatic PB is incurable. Standard treatment, including surgical resection, radiation, and systemic chemotherapy, improves survival but compromises neurocognitive function. A better understanding of the disease and the generation of preclinical models may enable re-evaluation of previous clinical trials, development of precision therapeutic strategi

Novel Echocardiographic Staging Classification for Cardiac Damage in Chronic Aortic Regurgitation.

BACKGROUND: Chronic aortic regurgitation (AR) is associated with significant cardiac remodeling, but the prevalence and prognostic impact of extravalvular cardiac damage remain unexplored. METHODS: Adults with moderate or greater chronic AR identified on echocardiogram between January 2008 and July 2024 were included. Exclusion criteria were acute AR, hypertrophic and infiltrative cardiomyopathies, prior cardiac surgery, and valve stenosis. Cardiac damage was classified into hierarchical stages: no cardiac damage (stage 0), left ventricular damage (stage 1), moderate or greater mitral regurgi

Oncovirus classification and insights into herpes simplex virus, Varicella-Zoster virus.

Even though the paper entitled: The role of oncolytic virotherapy and viral oncogenes in the cancer stem cells: a review of virus in cancer stem cells" provides informative details regarding viral contribution in cancer biology, its classification of Herpes Simplex Virus (HSV) and Varicella-Zoster Virus (VZV) as oncoviruses is inaccurate.Current evidence demonstrates that while HSV and VZV may reactivate in immunocompromised cancer patients, there is no causative relationship between these two viruses and oncogenesis. It is necessary to make a distinction between cancer-inducing viruses and re…

Feature map quantification: An efficient approach for active trachoma image classification.

Convolutional neural networks (CNNs) classify inverted eyelid images related to active trachoma. However, using these complex networks in me … ts reveal that VGG16 achieved an accuracy of 86.9 % (+0.43 % from the baseline) for active trachoma classification while reducing FLOPs by 28.6 % and parameters by 33.4 %. In the CIFAR10 classification, ResNet110 achieved an accuracy of 94.31 % (+0.73 % from the baseline) with a 43.8 % reduction in FLOPs and a 43.1 % reduction in parameters. Compared to state-of …

Tumor subtype classification tool for HPV-associated head and neck cancers.

BACKGROUND: Molecular subtypes of HPV-associated Head and Neck Squamous Cell Carcinoma (HNSCC), named IMU (immune strong) and KRT (highly keratinized), are well-recognized due to distinct molecular features, tumor microenvironments, clinical outcomes, and potentially differing optimal treatment strategies. Currently, no standardized method exists to subtype a new HPV + HNSCC tumor. Our paper introduces a machine learning-based classifier and webtool to reliably subtype HPV + HNSCC tumors using the IMU/KRT paradigm and highlights the importance of subtype in HPV + HNSCC. METHODS: We conducted

Quantitative ultrasound classification of healthy and chemically degraded ex-vivo cartilage.

In this study, we explore the potential of ten quantitative (radiofrequency-based) ultrasound parameters to assess the progressive loss of collagen and proteoglycans, mimicking an osteoarthritis condition in ex-vivo bovine cartilage samples. Most analyzed metrics showed significant changes as the degradation progressed, especially with collagenase treatment. We propose for the first time a combination of these ultrasound parameters through machine learning models aimed at automatically identifying healthy and degraded cartilage samples. The random forest model showed good performance in distin

Классификация тканей головного мозга с учетом микроструктуры на основе группирования количественных показателей МРТ.

Традиционные подходы к классификации тканей в vivo используют для сегментации воксельную силу традиционных клинических изображений магнитного резонанса (MR), которые не включают информацию о конкретных аспектах микроструктуры. С помощью системы кластеризации для анатомической количественной оценки и оценки (КАКО) количественные показатели МРТ могут использоваться для классификации тканей только на основе микроструктурных особенностей, не имеющих пространственного контроля, и могут оцениваться патологические изменения в заболеваниях. В этом исследовании для классификации тканей головного мозга

Совпадение в аннотации оригинала
Traditional tissue classification approaches in vivo use voxel intensities from conventional clinical magnetic resonance (MR) images for segmentation, which does not incorporate information about specific aspects of microstructure. With the Clustering for Anatomical Quantification and Evaluation (CAQE) framework, quantitative MRI measures can be used to clas…

Protecting Prostate Cancer Classification From Rectal Artifacts via Targeted Adversarial Training.

Magnetic resonance imaging (MRI)-based deep neural networks (DNN) have been widely developed to perform prostate cancer (PCa) classification. However, in real-world clinical situations, prostate MRIs can be easily impacted by rectal artifacts, which have been found to lead to incorrect PCa classification. Existing DNN-based methods typically do not consider the interference of rectal artifacts on PCa classification, and do not design specific strategy to address this problem. In this study, we proposed a novel Targeted adversarial training with Proprietary Adversarial Samples (TPAS) strategy t…

Functional connectivity-based classification of rapid eye movement sleep behavior disorder.

BACKGROUND: Isolated rapid eye movement sleep behavior disorder (iRBD) is a clinically important parasomnia syndrome preceding α-synucleinopathies, thereby prompting us to develop methods for evaluating latent brain states in iRBD. Resting-state functional magnetic resonance imaging combined with a machine learning-based classification technology may help us achieve this purpose. METHODS: We developed a machine learning-based classifier using functional connectivity to classify 55 patients with iRBD and 97 healthy elderly controls (HC). Selecting 55 HCs randomly from the HC dataset 100 times,…

Multimodal feature fusion in deep learning for comprehensive dental condition classification.

BACKGROUND: Dental health issues are on the rise, necessitating prompt and precise diagnosis. Automated dental condition classification can support this need. OBJECTIVE: The study aims to evaluate the effectiveness of deep learning methods and multimodal feature fusion techniques in advancing the field of automated dental condition classification. METHODS AND MATERIALS: A dataset of 11,653 clinically sourced images representing six prevalent dental conditions-caries, calculus, gingivitis, tooth discoloration, ulcers, and hypodontia-was utilized. Features were extracted using five Convolution…

Radiologic-Pathologic Correlation of Cardiac Tumors: Updated 2021 WHO Tumor Classification.

Cardiac tumors, although rare, carry high morbidity and mortality rates. They are commonly first identified either at echocardiography or incidentally at thoracoabdominal CT performed for noncardiac indications. Multimodality imaging often helps to determine the cause of these masses. Cardiac tumors comprise a distinct category in the World Health Organization (WHO) classification of tumors. The updated 2021 WHO classification of tumors of the heart incorporates new entities and reclassifies others. In the new classification system, papillary fibroelastoma is recognized as the most common prim…

Migraine headache (MH) classification using machine learning methods with data augmentation.

Migraine headache, a prevalent and intricate neurovascular disease, presents significant challenges in its clinical identification. Existing … hcare, utilizing advanced facilities ML has shown groundbreaking achievements related to developing classification and automatic predictors. With this, deep learning models, in particular, have proven effective in solving complex problems spanning computer vision and data analytics. Consequently, the integration of ML in healthcare has become vital, especially i …