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A mixture of experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of image blur.

BMC medical imaging
BACKGROUND: AI-based models for analysis of histopathology whole slide images (WSIs) are now common. However, image quality, particularly unsharp areas of WSIs, impacts model performance. In this study we investigate the impact of blur on deep learni...

Assessment of ChatGPT performance in orbital MRI reporting with multimetric evaluation of transformer based language models.

Scientific reports
Transformer-based large language models (LLMs), such as ChatGPT-4, are increasingly used to streamline clinical practice, of which radiology reporting is a prominent aspect. However, their performance in interpreting complex anatomical regions from M...

A multimodal multipath AI system for assessing PAH after VSD correction on echocardiography and chest radiography images.

Scientific reports
Developing a novel artificial intelligence (AI) system that can automatically detect pulmonary arterial hypertension (PAH) after correcting the ventricular septal defect (VSD) and to help clinicians make reasonable treatment plans. We analyzed data f...

The exhaled breath pattern as a potential method for biometrics identification.

Scientific reports
Conventional biometric identification methods relying on Personally Identifiable Information (PII) pose significant challenges concerning privacy and security. Volatile organic compounds (VOCs) in exhaled breath are unique to individuals and can serv...

Assessment of an unsupervised denoising approach based on Noise2Void in digital mammography.

Scientific reports
Full-field digital mammography (FFDM) is the most common imaging technique for breast cancer screening programs. Still, it is limited by noise from quantum effects, electronic issues, and X-ray scattering, affecting the image quality. Traditional den...

Streamlined and efficient patient-specific modeling for lumbar spine segmentation and finite element analysis.

Scientific reports
Advancing our understanding of spinal biomechanics through Finite Element Analysis (FEA) is essential for clinical decision-making and biomechanical research. Traditional FEA workflows are hindered by manual segmentation and meshing, introducing inco...

Predicting one-year overall survival in patients with AITL using machine learning algorithms: a multicenter study.

Scientific reports
Angioimmunoblastic T-cell lymphoma (AITL) is a life-threatening hematological malignancy. For patients with poor prognosis, especially those with expected survival less than 1 year, the benefits from traditional regimens are extremely limited. Theref...

Development of a serum protein biomarker panel for the diagnosis of pancreatic ductal adenocarcinoma using a machine learning approach.

Scientific reports
Early detection of pancreatic ductal adenocarcinoma (PDA) remains a major clinical challenge due to the lack of reliable biomarkers. We developed and validated a machine learning (ML)-based serum protein biomarker panel to enhance PDA diagnosis. Seru...

A radiomics model predicts progression from mild cognitive impairment to alzheimer's disease using structural MRI.

Scientific reports
The aim of this study is to build and validate a model based on structural magnetic resonance imaging (sMRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). A total of 343 patients with MCI were selected fro...

Research literacy and its predictors among university students and graduates identified by machine learning and spatial analysis.

Scientific reports
The landscape of academic publishing has evolved dramatically, leading to a surge in publications and journals. The 'publish or perish' culture has resulted in undesirable practices, such as many researchers publishing in predatory journals due to in...