Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis.

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges.

Anesthetic Management Recommendations Using a Machine Learning Algorithm to Reduce the Risk of Acute Kidney Injury After Cardiac Surgeries.

Background: Open heart surgeries are a common surgical approach among patients with heart disease. Acute kidney injury (AKI) is one of the most common postoperative complications following cardiac surgeries, with an average incidence of 6 - 10%. Additionally, AKI has a mortality rate of 5 - 10%.

Automated cardiac coverage assessment in cardiovascular magnetic resonance imaging using an explainable recurrent 3D dual-domain convolutional network.

Background: Cardiovascular magnetic resonance (CMR) imaging has become a modality with superior power for the diagnosis and prognosis of cardiovascular diseases. One of the essential quality controls of CMR images is to investigate the complete cardiac coverage, which is necessary for the volumetric and functional assessment.

Purpose: This study examines the full cardiac coverage using a 3D dual-domain convolutional model and then improves this model using an innovative explainable salient region detection model and a recurrent architecture.

A generalised deep meta-learning model for automated quality control of cardiovascular magnetic resonance images.

Background And Objectives: Cardiovascular magnetic resonance (CMR) imaging is a powerful modality in functional and anatomical assessment for various cardiovascular diseases. Sufficient image quality is essential to achieve proper diagnosis and treatment. A large number of medical images, the variety of imaging artefacts, and the workload of imaging centres are amongst the factors that reveal the necessity of automatic image quality assessment (IQA).

Prediction of Acute Kidney Injury After Cardiac Surgery Using Interpretable Machine Learning.

Background: Acute kidney injury (AKI) is a complication that occurs for various reasons after surgery, especially cardiac surgery. This complication can lead to a prolonged treatment process, increased costs, and sometimes death. Prediction of postoperative AKI can help anesthesiologists to implement preventive and early treatment strategies to reduce the risk of AKI.

Deep reinforcement learning in continuous action space for autonomous robotic surgery.

Purpose: Reinforcement learning methods have shown promising results for the automation of sub-tasks in robotic surgery systems. With the development of these methods, surgical robots have been able to achieve good performances, so that they can be used in complex and high-risk environments such as surgical pattern cutting to reduce stress and pressure on the surgeon and increase surgical accuracy. This study has aimed at providing a deep reinforcement learning-based approach to control the gripper arm when cutting soft tissue in a continuous action space.

Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis study.

Purpose: Deep learning (DL) has led to widespread changes in automated segmentation and classification for medical purposes. This study is an attempt to use statistical methods to analyze studies related to segmentation and classification of head and neck cancers (HNCs) and brain tumors in MRI images.

Methods: PubMed, Web of Science, Embase, and Scopus were searched to retrieve related studies published from January 2016 to January 2020.

A Random Walk Approach to Query Informative Constraints for Clustering.

This paper presents a random walk approach to the problem of querying informative constraints for clustering. The proposed method is based on the properties of the commute time, that is the expected time taken for a random walk to travel between two nodes and return, on the adjacency graph of data. Commute time has the nice property of that, the more short paths connect two given nodes in a graph, the more similar those nodes are.

Querying Beneficial Constraints Before Clustering Using Facility Location Analysis.

This paper examines the problem of querying beneficial constraints before clustering. Existing methods in this area choose constraints heuristically based on some prior assumptions on the usefulness of constraints. However, the usefulness and propagation of constraints are two important issues in the constraints selection that are not investigated simultaneously in most existing works.