Study of Power Equivalent Continuous Approximation Based on the Recent Consensus Recommendations for Brain Tumor Imaging with Pulsed Chemical Exchange Saturation Transfer at 3T.

The quantitative analysis of pulsed-chemical exchange saturation transfer (CEST) using a full model-based method is computationally challenging, as it involves dealing with varying RF values in pulsed saturation. A power equivalent continuous approximation of B power was usually applied to accelerate the analysis. In line with recent consensus recommendations from the CEST community for pulsed-CEST at 3T, particularly recommending a high RF saturation power (B = 2.

Predictive Modeling of COVID-19 Readmissions: Insights from Machine Learning and Deep Learning Approaches.

This project employs artificial intelligence, including machine learning and deep learning, to assess COVID-19 readmission risk in Malaysia. It offers tools to mitigate healthcare resource strain and enhance patient outcomes. This study outlines a methodology for classifying COVID-19 readmissions.

A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model.

Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes.

Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review.

Syncope is a transient loss of consciousness with rapid onset. The aims of the study were to systematically evaluate available machine learning (ML) algorithm for supporting syncope diagnosis to determine their performance compared to existing point scoring protocols. We systematically searched IEEE Xplore, Web of Science, and Elsevier for English articles (Jan 2011 - Sep 2021) on individuals aged five and above, employing ML algorithms in syncope detection with Head-up titl table test (HUTT)-monitored hemodynamic parameters and reported metrics.

Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images.

Debates persist regarding the impact of Stain Normalization (SN) on recent breast cancer histopathological studies. While some studies propose no influence on classification outcomes, others argue for improvement. This study aims to assess the efficacy of SN in breast cancer histopathological classification, specifically focusing on Invasive Ductal Carcinoma (IDC) grading using Convolutional Neural Networks (CNNs).

Non-annotated renal histopathological image analysis with deep ensemble learning.

Background: Renal cancer is one of the leading causes of cancer-related deaths worldwide, and early detection of renal cancer can significantly improve the patients' survival rate. However, the manual analysis of renal tissue in the current clinical practices is labor-intensive, prone to inter-pathologist variations and easy to miss the important cancer markers, especially in the early stage.

Methods: In this work, we developed deep convolutional neural network (CNN) based heterogeneous ensemble models for automated analysis of renal histopathological images without detailed annotations.

Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning.

Gastric cancer is a leading cause of cancer-related deaths worldwide, underscoring the need for early detection to improve patient survival rates. The current clinical gold standard for detection is histopathological image analysis, but this process is manual, laborious, and time-consuming. As a result, there has been growing interest in developing computer-aided diagnosis to assist pathologists.

An Overview of Deep Learning Methods for Left Ventricle Segmentation.

Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images.

Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images.

Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning.

Amide proton transfer imaging in stroke.

Amide proton transfer (APT) imaging, a variant of chemical exchange saturation transfer MRI, has shown promise in detecting ischemic tissue acidosis following impaired aerobic metabolism in animal models and in human stroke patients due to the sensitivity of the amide proton exchange rate to changes in pH within the physiological range. Recent studies have demonstrated the possibility of using APT-MRI to detect acidosis of the ischemic penumbra, enabling the assessment of stroke severity and risk of progression, monitoring of treatment progress, and prognostication of clinical outcome. This paper reviews current APT imaging methods actively used in ischemic stroke research and explores the clinical aspects of ischemic stroke and future applications for these methods.

Magnetic Resonance pH Imaging in Stroke - Combining the Old With the New.

The study of stroke has historically made use of traditional spectroscopy techniques to provide the ground truth for parameters like pH. However, techniques like P spectroscopy have limitations, in particular poor temporal and spatial resolution, coupled with a need for a high field strength and specialized coils. More modern magnetic resonance spectroscopy (MRS)-based imaging techniques like chemical exchange saturation transfer (CEST) have been developed to counter some of these limitations but lack the definitive gold standard for pH that P spectroscopy provides.

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis.

Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. Therefore, to overcome the limitations of the commonly used method as above, numerous deep learning approaches, especially the convolutional neural network (CNN), have been developed to improve the clinical workflow efficiency.

Transcranial Electrical Motor Evoked Potential in Predicting Positive Functional Outcome of Patients after Decompressive Spine Surgery: Review on Challenges and Recommendations towards Objective Interpretation.

Spine surgeries impose risk to the spine's surrounding anatomical and physiological structures especially the spinal cord and the nerve roots. Intraoperative neuromonitoring (IONM) is a technology developed to monitor the integrity of the spinal cord and the nerve roots via the surgery. Transcranial motor evoked potential (TcMEP) (one of the IONM modalities) is adopted to monitor the integrity of the motor pathway of the spinal cord and the motor nerve roots.

Current Trends in Readmission Prediction: An Overview of Approaches.

Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission.

Clinical translation of amide proton transfer (APT) MRI for ischemic stroke: a systematic review (2003-2020).

Amide proton transfer (APT) magnetic resonance imaging (MRI) is a pH-sensitive imaging technique that can potentially complement existing clinical imaging protocol for the assessment of ischemic stroke. This review aims to summarize the developments in the clinical research of APT imaging of ischemic stroke after 17 years of progress since its first preclinical study in 2003. Three electronic databases: PubMed, Scopus, and Cochrane Library were systematically searched for articles reporting clinical studies on APT imaging of ischemic stroke.

Study of common quantification methods of amide proton transfer magnetic resonance imaging for ischemic stroke detection.

Purpose: To assess the correlation and differences between common amide proton transfer (APT) quantification methods in the diagnosis of ischemic stroke.

Methods: Five APT quantification methods, including asymmetry analysis and its variants as well as two Lorentzian model-based methods, were applied to data acquired from six rats that underwent middle cerebral artery occlusion scanned at 9.4T.

Analysis of model-based and model-free CEST effect quantification methods for different medical applications.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) holds great potential to provide new metabolic information for clinical applications such as tumor, stroke and Parkinson's Disease diagnosis. Many active research and developments have been conducted to translate this emerging MRI technique for routine clinical applications. In general, there are two CEST quantification techniques: (i) model-free and (ii) model-based techniques.

Hierarchical gated recurrent neural network with adversarial and virtual adversarial training on text classification.

Document classification aims to assign one or more classes to a document for ease of management by understanding the content of a document. Hierarchical attention network (HAN) has been showed effective to classify documents that are ambiguous. HAN parses information-intense documents into slices (i.

Identifying the ischaemic penumbra using pH-weighted magnetic resonance imaging.

The original concept of the ischaemic penumbra suggested imaging of regional cerebral blood flow and metabolism would be required to identify tissue that may benefit from intervention. Amide proton transfer magnetic resonance imaging, a chemical exchange saturation transfer technique, has been used to derive cerebral intracellular pH in preclinical stroke models and has been proposed as a metabolic marker of ischaemic penumbra. In this proof of principle clinical study, we explored the potential of this pH-weighted magnetic resonance imaging technique at tissue-level.

Quantification of amide proton transfer effect pre- and post-gadolinium contrast agent administration.

Purpose: To compare quantification of the amide proton transfer (APT) effect pre- and post-gadolinium contrast agent (Gd) administration in order to establish to what extent Gd alters quantification of the APT effect.

Materials And Methods: Four patients with internal carotid stenosis were recruited. APT imaging was acquired pre- and post-contrast in two sessions (before and after surgery) to assess the extent of relaxation time, T1 , change on APT effect calculated using magnetization transfer ratio asymmetry analysis at offsets of ±3.

Quantitative Bayesian model-based analysis of amide proton transfer MRI.

Amide Proton Transfer (APT) reports on contrast derived from the exchange of protons between amide groups and water. Commonly, APT contrast is quantified by asymmetry analysis, providing an ensemble contrast of both amide proton concentration and exchange rate. An alternative is to sample the off-resonant spectrum and fit an exchange model, permitting the APT effect to be quantified, correcting automatically for confounding effects of spillover, field inhomogeneity, and magnetization transfer.