Quantitative evaluation of Saliency-Based Explainable artificial intelligence (XAI) methods in Deep Learning-Based mammogram analysis.

Background: Explainable Artificial Intelligence (XAI) is prominent in the diagnostics of opaque deep learning (DL) models, especially in medical imaging. Saliency methods are commonly used, yet there's a lack of quantitative evidence regarding their performance.

Objectives: To quantitatively evaluate the performance of widely utilized saliency XAI methods in the task of breast cancer detection on mammograms.

Awareness and level of digital literacy among students receiving health-based education.

Background: Being digitally literate allows health-based science students to access reliable, up-to-date information efficiently and expands the capacity for continuous learning. Digital literacy facilitates effective communication and collaboration among other healthcare providers. It helps to navigate the ethical implications of using digital technologies and aids the use of digital tools in managing healthcare processes.

Robot-Assisted Minimally Invasive Multivessel Coronary Bypass Guided by Computerized Tomography.

Objective: Robot-assisted minimally invasive coronary bypass surgery is one of the least invasive approaches that offers multivessel revascularization and accelerated recovery. We investigated the benefits of computed tomography angiography (CTA) guidance in robotic coronary bypass (RCAB) by analyzing perioperative outcomes.

Methods: Between April 2022 and April 2023, 60 consecutive patients who underwent RCAB under preoperative CTA guidance were included.

Deep learning for assessing image quality in bi-parametric prostate MRI: A feasibility study.

Background: Although systems such as Prostate Imaging Quality (PI-QUAL) have been proposed for quality assessment, visual evaluations by human readers remain somewhat inconsistent, particularly among less-experienced readers.

Objectives: To assess the feasibility of deep learning (DL) for the automated assessment of image quality in bi-parametric MRI scans and compare its performance to that of less-experienced readers.

Methods: We used bi-parametric prostate MRI scans from the PI-CAI dataset in this study.

Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study.

Objective: To evaluate the effectiveness of a self-adapting deep network, trained on large-scale bi-parametric MRI data, in detecting clinically significant prostate cancer (csPCa) in external multi-center data from men of diverse demographics; to investigate the advantages of transfer learning.

Methods: We used two samples: (i) Publicly available multi-center and multi-vendor Prostate Imaging: Cancer AI (PI-CAI) training data, consisting of 1500 bi-parametric MRI scans, along with its unseen validation and testing samples; (ii) In-house multi-center testing and transfer learning data, comprising 1036 and 200 bi-parametric MRI scans. We trained a self-adapting 3D nnU-Net model using probabilistic prostate masks on the PI-CAI data and evaluated its performance on the hidden validation and testing samples and the in-house data with and without transfer learning.

Inter-reader agreement of the prostate imaging quality (PI-QUAL) score for basic readers in prostate MRI: A multi-center study.

Background: The Prostate Imaging Quality (PI-QUAL) score is the first step toward image quality assessment in multi-parametric prostate MRI (mpMRI). Previous studies have demonstrated moderate to excellent inter-rater agreement among expert readers; however, there is a need for studies to assess the inter-reader agreement of PI-QUAL scoring in basic prostate readers.

Objectives: To assess the inter-reader agreement of the PI-QUAL score amongst basic prostate readers on multi-center prostate mpMRI.

Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study.

The use of deep learning (DL) techniques for automated diagnosis of large vessel occlusion (LVO) and collateral scoring on computed tomography angiography (CTA) is gaining attention. In this study, a state-of-the-art self-configuring object detection network called nnDetection was used to detect LVO and assess collateralization on CTA scans using a multi-task 3D object detection approach. The model was trained on single-phase CTA scans of 2425 patients at five centers, and its performance was evaluated on an external test set of 345 patients from another center.

The Role of Whole-Body Magnetic Resonance Imaging in Assessing Extrapulmonary Metastases in Osteosarcoma Staging and Restaging: A Pilot Study.

Objective: The aim of the study is to investigate the role of whole-body magnetic resonance imaging (MRI) in assessing extrapulmonary metastases in primary osteosarcoma staging.

Methods: We retrospectively reviewed medical data to identify primary osteosarcoma patients with available preoperative whole-body MRI obtained in the staging or restaging. Histopathology was the reference test for assessing the diagnostic performance, if available.

Does deep learning software improve the consistency and performance of radiologists with various levels of experience in assessing bi-parametric prostate MRI?

Objective: To investigate whether commercially available deep learning (DL) software improves the Prostate Imaging-Reporting and Data System (PI-RADS) scoring consistency on bi-parametric MRI among radiologists with various levels of experience; to assess whether the DL software improves the performance of the radiologists in identifying clinically significant prostate cancer (csPCa).

Methods: We retrospectively enrolled consecutive men who underwent bi-parametric prostate MRI at a 3 T scanner due to suspicion of PCa. Four radiologists with 2, 3, 5, and > 20 years of experience evaluated the bi-parametric prostate MRI scans with and without the DL software.

Outcome Predictors of Lesional Posterior Cortex Epilepsy Surgery.

Background: Lesional posterior cortex epilepsy (PCE) is often drug resistant and may benefit from surgical intervention. In this study, we aimed to identify potential predictive factors associated with seizure recurrence after epilepsy surgery in lesional PCE.

Methods: We retrospectively reviewed patients with PCE who underwent surgery between 1998 and 2021.

A Comparative Study of Multiparametric MRI Sequences in Measuring Prostate Cancer Index Lesion Volume.

Objectives: To compare the effectiveness of individual multiparametric prostate MRI (mpMRI) sequences-T2W, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE)-in assessing prostate cancer (PCa) index lesion volume using whole-mount pathology as the ground-truth; to assess the impact of an endorectal coil (ERC) on the measurements.

Materials And Methods: We retrospectively enrolled 72 PCa patients who underwent 3T mpMRI with (n = 39) or without (n = 33) an ERC. A pathologist drew the index lesion borders on whole-mount pathology using planimetry (whole-mount).

The analysis of F-FDG PET/MRI, electroencephalography, and semiology in patients with gray matter heterotopia: A pilot study.

Objectives: To describe F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging ( F-FDG PET/MRI) along with semiology and electroencephalography (EEG) in patients with gray matter heterotopia (GMH); to evaluate the concordance between F-FDG PET/MRI and clinical epileptogenic zone (EZ).

Materials & Methods: GMH (subcortical heterotopia [SCH] and periventricular nodular heterotopia [PNH]) patients with epilepsy who underwent F-FDG PET/MRI were retrospectively enrolled. Two radiologists evaluated brain MRI, while two nuclear medicine specialists assessed the F-FDG PET.

Performance of apparent diffusion coefficient values and ratios for the prediction of prostate cancer aggressiveness across different MRI acquisition settings.

Purpose: In this study, we assessed the performance of apparent diffusion coefficient (ADC) and diffusion-weighted imaging (DWI) metrics and their ratios across different magnetic resonance imaging (MRI) acquisition settings, with or without an endorectal coil (ERC), for the evaluation of prostate cancer (PCa) aggressiveness using whole-mount specimens as a reference.

Methods: We retrospectively reviewed the data of prostate carcinoma patients with a Gleason score (GS) of 3+4 or higher who underwent prostate MRI using a 3T unit at our institution. They were divided into two groups based on the use of ERC for MRI acquisition, and patients who underwent prostate MRI with an ERC constituted the ERC (n = 55) data set, while the remaining patients accounted for the non-ERC data set (n = 41).

A joint convolutional-recurrent neural network with an attention mechanism for detecting intracranial hemorrhage on noncontrast head CT.

To investigate the performance of a joint convolutional neural networks-recurrent neural networks (CNN-RNN) using an attention mechanism in identifying and classifying intracranial hemorrhage (ICH) on a large multi-center dataset; to test its performance in a prospective independent sample consisting of consecutive real-world patients. All consecutive patients who underwent emergency non-contrast-enhanced head CT in five different centers were retrospectively gathered. Five neuroradiologists created the ground-truth labels.

Third-Generation Dual-Source Computed Tomography for Coronary Angiography With Individually Tailored Scan Protocols Can Achieve a Low Radiation Dose With Good Image Quality in Unselected Patients.

Objective: To explore the image quality and radiation exposure associated with coronary angiography obtained with a third-generation dual-source computed tomography, using body mass index (BMI)- and heart rate (HR)-adapted protocols in real-world patients.

Methods: Three scan protocols were implemented with regard to HR: prospective turbo high-pitch spiral, sequential, and retrospective spiral modes. We adapted the reference kilovoltage value according to BMI.

The Association between the Extent of Late Gadolinium Enhancement and Diastolic Dysfunction in Hypertrophic Cardiomyopathy.

Diastolic dysfunction in hypertrophic cardiomyopathy (HCM) patients is a frequent, yet poorly understood phenomenon. The purpose of this study is to assess the relationship between the myocardial fibrosis and diastolic dysfunction in patients with HCM. We retrospectively investigated the impact of the myocardial fibrosis, as assessed by the extent of late gadolinium enhancement (LGE-%) on cardiac magnetic resonance imaging (CMRI), on diastolic dysfunction in 110 patients with HCM.

Is neutrophile to lymphocyte ratio a predictor of Gleason score upgrading according to risk classifications in patients with prostate cancer.

Objectives: We aimed to determine the parameters that predict Gleason Score (GS) upgrading in patients undergoing robot-assisted laparoscopic radical prostatectomy (RARP) and especially the ability of neutrophile to lymphocyte ratio (NLR) in predicting the upgrading.

Methods: Patients who underwent RARP for prostate cancer in our clinic between January 2013 and January 2018 were retrospectively analyzed. Patients' demographic data, preoperative and postoperative parameters were all recorded in the database.

Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study.

There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively included DWI data of patients with acute ischemic lesions from six centers. Dataset A (n = 2986) and B (n = 3951) included data from Siemens and GE MRI scanners, respectively.

The visibility of prostate cancer concerning underlying histopathological variances: A single-center multiparametric magnetic resonance imaging study.

Purpose: To investigate whether prostate cancer (PCa) lesions regarding histopathological composition exhibit different morphological features on multiparametric prostate MRI (mpMRI).

Methods: We investigated men with PCa with available mpMRI and whole-mount specimens between June 2015 to December 2020.The acquisition protocol consistent with the Prostate Imaging Reporting and Data System (PI-RADS).

Does Employing a Flowchart Improve the Diagnostic Performance of Cardiac Magnetic Resonance Imaging in Left Ventricular Noncompaction?

Background: To test the hypothesis that making a diagnosis of left ventricular noncompaction (LVNC) on cardiac magnetic resonance imaging (CMRI) using a noncompacted-to-compacted (NC/C) myocardium ratio > 2.3 would yield significant errors, and also to test a diagnostic flowchart in patients who undergo CMRI and have clinical and echocardiographic findings suggesting LVNC could improve the diagnosis of LVNC.

Methods: A total of 84 patients with LVNC and 162 controls consisting of patients with other diseases and healthy participants who had CMRI and echocardiograms were selected.

Epicardial adipose tissue volume predicts long term major adverse cardiovascular events in patients with Type 2 diabetes.

Objective: Epicardial adipose tissue (EAT) is a metabolically active visceral fat depot that plays an important role in coronary atherosclerosis. In this study, our aim was to investigate the relationship between long-term major adverse cardiovascular events (MACEs) and EAT volume detected by coronary computed tomography angiography (CCTA) in patients with Type 2 diabetes mellitus (T2-DM) without previous coronary events.

Methods: A total of 127 patients with diabetes who underwent CCTA between 2012 and 2014 were enrolled retrospectively.

Association between left ventricular strain and cardiac iron load in beta-thalassaemia major: a cardiac magnetic resonance study.

Objective: To evaluate the utility of cardiac magnetic resonance feature tracking-derived left ventricular strain in assessing cardiac dysfunction and investigate the correlation between left ventricular strain and myocardial T2* in patients with beta-thalassaemia major.

Methods: Forty-two patients with beta-thalassaemia major, having a mean age of 22.49 ± 8.