Out-of-distribution generalization for segmentation of lymph node metastasis in breast cancer.

Pathology provides the definitive diagnosis, and Artificial Intelligence (AI) tools are poised to improve accuracy, inter-rater agreement, and turn-around time (TAT) of pathologists, leading to improved quality of care. A high value clinical application is the grading of Lymph Node Metastasis (LNM) which is used for breast cancer staging and guides treatment decisions. A challenge of implementing AI tools widely for LNM classification is domain shift, where Out-of-Distribution (OOD) data has a different distribution than the In-Distribution (ID) data used to train the model, resulting in a drop in performance in OOD data.

Leveraging domain knowledge for synthetic ultrasound image generation: a novel approach to rare disease AI detection.

Purpose: This study explores the use of deep generative models to create synthetic ultrasound images for the detection of hemarthrosis in hemophilia patients. Addressing the challenge of sparse datasets in rare disease diagnostics, the study aims to enhance AI model robustness and accuracy through the integration of domain knowledge into the synthetic image generation process.

Methods: The study employed two ultrasound datasets: a base dataset (Db) of knee recess distension images from non-hemophiliac patients and a target dataset (Dt) of hemarthrosis images from hemophiliac patients.

Towards a histological diagnosis of childhood small vessel CNS vasculitis.

Background: Primary small vessel CNS vasculitis (sv-cPACNS) is a challenging inflammatory brain disease in children. Brain biopsy is mandatory to confirm the diagnosis. This study aims to develop and validate a histological scoring tool for diagnosing small vessel CNS vasculitis.

Utilizing artificial intelligence for the detection of hemarthrosis in hemophilia using point-of-care ultrasonography.

Background: Recurrent hemarthrosis and resultant hemophilic arthropathy are significant causes of morbidity in persons with hemophilia, despite the marked evolution of hemophilia care. Prevention, timely diagnosis, and treatment of bleeding episodes are key. However, a physical examination or a patient's assessment of musculoskeletal pain may not accurately identify a joint bleed.

Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease.

Using diffusion models to generate synthetic labeled data for medical image segmentation.

Purpose: Medical image analysis has become a prominent area where machine learning has been applied. However, high-quality, publicly available data are limited either due to patient privacy laws or the time and cost required for experts to annotate images. In this retrospective study, we designed and evaluated a pipeline to generate synthetic labeled polyp images for augmenting medical image segmentation models with the aim of reducing this data scarcity.

Impact of Automated Prognostication on Traumatic Brain Injury Care: A Focus Group Study.

Background: Prognosticating outcomes for traumatic brain injury (TBI) patients is challenging due to the required specialized skills and variability among clinicians. Recent attempts to standardize TBI prognosis have leveraged machine learning (ML) methodologies. This study evaluates the necessity and influence of ML-assisted TBI prognostication through healthcare professionals' perspectives via focus group discussions.

Development of a Multimodal Machine Learning-Based Prognostication Model for Traumatic Brain Injury Using Clinical Data and Computed Tomography Scans: A CENTER-TBI and CINTER-TBI Study.

Computed tomography (CT) is an important imaging modality for guiding prognostication in patients with traumatic brain injury (TBI). However, because of the specialized expertise necessary, timely and dependable TBI prognostication based on CT imaging remains challenging. This study aimed to enhance the efficiency and reliability of TBI prognostication by employing machine learning (ML) techniques on CT images.

Neuroimaging Scoring Tools to Differentiate Inflammatory Central Nervous System Small-Vessel Vasculitis: A Need for Artificial Intelligence/Machine Learning?-A Scoping Review.

Neuroimaging has a key role in identifying small-vessel vasculitis from common diseases it mimics, such as multiple sclerosis. Oftentimes, a multitude of these conditions present similarly, and thus diagnosis is difficult. To date, there is no standardized method to differentiate between these diseases.

Approaches and Limitations of Machine Learning for Synthetic Ultrasound Generation: A Scoping Review.

This scoping review examines the emerging field of synthetic ultrasound generation using machine learning (ML) models in radiology. Nineteen studies were analyzed, revealing three primary methodological strategies: unconditional generation, conditional generation, and domain translation. Synthetic ultrasound is mainly used to augment training datasets and as training material for radiologists.

The role of cortisol in immunosuppression in subarachnoid haemorrhage.

Background: We sought to determine the extent to which cortisol suppressed innate and T cell-mediated cytokine production and whether it could be involved in reducing peripheral cytokine production following subarachnoid haemorrhage (SAH).

Methods: Whole blood from healthy controls, patients with SAH and healthy volunteers was stimulated with lipopolysaccharide (LPS), to stimulate innate immunity, or phytohaemagglutinin (PHA), to stimulate T cell-mediated immunity. Varying concentrations of cortisol were included, with or without the cortisol antagonist RU486.

Analyzing Barriers and Enablers for the Acceptance of Artificial Intelligence Innovations into Radiology Practice: A Scoping Review.

Objectives: This scoping review was conducted to determine the barriers and enablers associated with the acceptance of artificial intelligence/machine learning (AI/ML)-enabled innovations into radiology practice from a physician's perspective.

Methods: A systematic search was performed using Ovid Medline and Embase. Keywords were used to generate refined queries with the inclusion of computer-aided diagnosis, artificial intelligence, and barriers and enablers.

Immunoassay for detection of coproantigen in dogs and cats.

infections in dogs and cats are underestimated because of a lack of proglottid observations and poor recovery of parasite elements by centrifugal flotation. We developed an immunoassay that employs a pair of monoclonal antibodies to capture specific coproantigen in fecal extracts from dogs and cats. Real-time PCR for DNA in perianal swabs and observation of proglottids were used as reference methods.

Automated screening of computed tomography using weakly supervised anomaly detection.

Background: Current artificial intelligence studies for supporting CT screening tasks depend on either supervised learning or detecting anomalies. However, the former involves a heavy annotation workload owing to requiring many slice-wise annotations (ground truth labels); the latter is promising, but while it reduces the annotation workload, it often suffers from lower performance. This study presents a novel weakly supervised anomaly detection (WSAD) algorithm trained based on scan-wise normal and anomalous annotations to provide better performance than conventional methods while reducing annotation workload.

Can Machine Learning Be Better than Biased Readers?

Training machine learning (ML) models in medical imaging requires large amounts of labeled data. To minimize labeling workload, it is common to divide training data among multiple readers for separate annotation without consensus and then combine the labeled data for training a ML model. This can lead to a biased training dataset and poor ML algorithm prediction performance.

Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics.

Supervised machine learning classification is the most common example of artificial intelligence (AI) in industry and in academic research. These technologies predict whether a series of measurements belong to one of multiple groups of examples on which the machine was previously trained. Prior to real-world deployment, all implementations need to be carefully evaluated with hold-out validation, where the algorithm is tested on different samples than it was provided for training, in order to ensure the generalizability and reliability of AI models.

Imaging of Lower Limb Tumors and Tumor-Like Conditions.

Bone and soft tissue lesions are frequently seen in the lower limbs. Many are non-neoplastic but may mimic tumours. In this article, we discuss a practical approach for the diagnosis and management of the most common tumours and tumour-like conditions seen in the lower limbs.

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

Background: The purpose of this study was to conduct a systematic review for understanding the availability and limitations of artificial intelligence (AI) approaches that could automatically identify and quantify computed tomography (CT) findings in traumatic brain injury (TBI).

Methods: Systematic review, in accordance with PRISMA 2020 and SPIRIT-AI extension guidelines, with a search of 4 databases (Medline, Embase, IEEE Xplore, and Web of Science) was performed to find AI studies that automated the clinical tasks for identifying and quantifying CT findings of TBI-related abnormalities.

Results: A total of 531 unique publications were reviewed, which resulted in 66 articles that met our inclusion criteria.

α2-fraction and haptoglobin as biomarkers for disease activity in oligo- and polyarticular juvenile idiopathic arthritis.

Objectives: Unlike in adult rheumatology, for most forms of juvenile idiopathic arthritis (JIA) no reliable biomarkers currently exist to assess joint and disease activity. However, electrophoresis is frequently found changed in active juvenile arthritis. The objective of this study was to evaluate the α2-fraction of serum electrophoresis and its main components as biomarkers for JIA, categories extended/persistent oligoarthritis and seronegative polyarthritis, in comparison with the conventionally used erythrocyte sedimentation rate and C-reactive protein.

Magnetic Resonance Elastography-derived Stiffness Predicts Renal Function Loss and Is Associated With Microvascular Inflammation in Kidney Transplant Recipients.

Background: Organ stiffening can be caused by inflammation and fibrosis, processes that are common causes of transplant kidney dysfunction. Magnetic resonance elastography (MRE) is a contrast-free, noninvasive imaging modality that measures kidney stiffness. The objective of this study was to assess the ability of MRE to serve as a prognostic factor for renal outcomes.

External Validation of O-RADS US Risk Stratification and Management System.

Background The Ovarian-Adnexal Reporting and Data System (O-RADS) US risk stratification and management system (O-RADS US) was designed to improve risk assessment and management of ovarian and adnexal lesions. Validation studies including both surgical and nonsurgical treatment as the reference standard remain lacking. Purpose To externally validate O-RADS US in women who underwent either surgical or nonsurgical treatment and to determine if incorporating acoustic shadowing as a benign finding improves diagnostic performance.