Intelligence model on sequence-based prediction of PPI using AISSO deep concept with hyperparameter tuning process.

Protein-protein interaction (PPI) prediction is vital for interpreting biological activities. Even though many diverse sorts of data and machine learning approaches have been employed in PPI prediction, performance still has to be enhanced. As a result, we adopted an Aquilla Influenced Shark Smell (AISSO)-based hybrid prediction technique to construct a sequence-dependent PPI prediction model.

PENet: Continuous-Valued Pulmonary Edema Severity Prediction On Chest X-ray Using Siamese Convolutional Networks.

For physicians to make rapid clinical decisions for patients with congestive heart failure, the assessment of pulmonary edema severity in chest radiographs is vital. Although deep learning has shown promise in detecting the presence or absence or discrete grades of severity, of such edema, prediction of continuous-valued severity yet remains a challenge. Here, we propose PENet: Siamese convolutional neural networks to assess the continuous spectrum of severity of lung edema from chest radiographs.

Impact of Follow-Up Imaging Recommendation Specificity on Adherence.

Radiology reports often contain follow-up imaging recommendations, but failure to comply with them in a timely manner can lead to delayed treatment, poor patient outcomes, complications, and legal liability. Using a dataset containing 2,972,164 exams for over 7 years, in this study we explored the association between recommendation specificity on follow-up rates. Our results suggest that explicitly mentioning the follow-up interval as part of a follow-up imaging recommendation has a significant impact on adherence making these recommendations 3 times more likely (95% CI: 2.

Lung-RADS Version 1.0 versus Lung-RADS Version 1.1: Comparison of Categories Using Nodules from the National Lung Screening Trial.

Background The American College of Radiology updated Lung Imaging Reporting and Data System (Lung-RADS) version 1.0 to version 1.1 in May 2019, with the two key changes involving perifissural nodules (PFNs) and ground-glass nodules (GGNs) now designated as a negative screening result.

Deep Learning to Quantify Pulmonary Edema in Chest Radiographs.

Purpose: To develop a machine learning model to classify the severity grades of pulmonary edema on chest radiographs.

Materials And Methods: In this retrospective study, 369 071 chest radiographs and associated radiology reports from 64 581 patients (mean age, 51.71 years; 54.

Determining Follow-Up Imaging Study Using Radiology Reports.

Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to delayed treatment, poor patient outcomes, complications, unnecessary testing, lost revenue, and legal liability. The objective of this study was to develop a scalable approach to automatically identify the completion of a follow-up imaging study recommended by a radiologist in a preceding report.

Automated Tracking of Follow-Up Imaging Recommendations.

Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to poor patient outcomes, complications, and legal liability. As such, the primary objective of this research was to determine adherence rates to follow-up recommendations.

Vancouver Risk Calculator Compared with ACR Lung-RADS in Predicting Malignancy: Analysis of the National Lung Screening Trial.

Purpose To compare the Vancouver risk calculator (VRC) with American College of Radiology (ACR) Lung Imaging Reporting and Data System (Lung-RADS) in predicting the risk of malignancy in the National Lung Screening Trial (NLST). Materials and Methods A total of 2813 patients with 4408 nodules (4078 solid, 330 subsolid) were available from the NLST for evaluation. Nodules were scored by using VRC with nine parameters (output was the percentage likelihood of malignancy; VRC threshold for malignancy likelihood set as greater than 5%) and Lung-RADS (output was category 2-4B; malignancy defined as category 4A or 4B; malignancy likelihood greater than 5%).

Determining Adherence to Follow-up Imaging Recommendations.

Purpose: Radiology reports often contain follow-up imaging recommendations. However, these recommendations are not always followed up by referring physicians and patients. Failure to comply in a timely manner can lead to delayed treatment, poor patient outcomes, unnecessary testing, lost revenue, and legal liability.

Extracting Follow-Up Recommendations and Associated Anatomy from Radiology Reports.

Adherence rates for timely imaging follow-up are usually low due to low rates of diligence by referring physicians and/or patients with following recommendations for follow-up imaging. This can lead to delayed treatment, poor patient outcomes, unnecessary testing, and legal liability. Existing follow-up recommendation detection methods are often disease- and modality-specific.

Real-time FDG PET guidance during biopsies and radiofrequency ablation using multimodality fusion with electromagnetic navigation.

Purpose: To assess the feasibility of combined electromagnetic device tracking and computed tomography (CT)/ultrasonography (US)/fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) fusion for real-time feedback during percutaneous and intraoperative biopsies and hepatic radiofrequency (RF) ablation.

Materials And Methods: In this HIPAA-compliant, institutional review board-approved prospective study with written informed consent, 25 patients (17 men, eight women) underwent 33 percutaneous and three intraoperative biopsies of 36 FDG-avid targets between November 2007 and August 2010. One patient underwent biopsy and RF ablation of an FDG-avid hepatic focus.

Intra-operative volume imaging of the left atrium and pulmonary veins with rotational X-ray angiography.

Complex electrophysiology (EP) procedures, such as catheter-based ablation in the left atrium and pulmonary veins (LAPV) for treatment of atrial fibrillation, require knowledge of heart chamber anatomy. Electroanatomical mapping (EAM) is typically used to define cardiac structures by combining electromagnetic spatial catheter localization with surface models which interpolate the anatomy between EAM point locations in 3D. Recently, the incorporation of pre-operative volumetric CT or MR data sets has allowed for more detailed maps of LAPV anatomy to be used intra-operatively.