Improved Detection Accuracy of Chronic Vertebral Compression Fractures by Integrating Height Loss Ratio and Deep Learning Approaches.

Objectives: This study aims to assess the limitations of the height loss ratio (HLR) method and introduce a new approach that integrates a deep learning (DL) model to enhance vertebral compression fracture (VCF) detection performance.

Methods: We conducted a retrospective study on 589 patients with chronic VCFs. We compared four different methods: HLR-only, DL-only, a combination of HLR and DL for positive VCF, and a combination of HLR and DL for negative VCF.

Automated deep learning-based bone mineral density assessment for opportunistic osteoporosis screening using various CT protocols with multi-vendor scanners.

This retrospective study examined the diagnostic efficacy of automated deep learning-based bone mineral density (DL-BMD) measurements for osteoporosis screening using 422 CT datasets from four vendors in two medical centers, encompassing 159 chest, 156 abdominal, and 107 lumbar spine datasets. DL-BMD values on L1 and L2 vertebral bodies were compared with manual BMD (m-BMD) measurements using Pearson's correlation and intraclass correlation coefficients. Strong agreement was found between m-BMD and DL-BMD in total CT scans (r = 0.

Associations of Longitudinal Multiparametric MRI Findings and Clinical Outcomes in Intra-Articular Injections for Knee Osteoarthritis.

Background: Osteoarthritis (OA) is a complex disease marked by the degradation of articular cartilage.

Objective: This study aimed to explore the relationship between cartilage volume/thickness and clinical outcomes in knee OA patients treated with intra-articular injections over one year.

Methods: Twenty-four patients with mild-to-moderate OA were retrospectively analyzed using knee MRI.

Automated Opportunistic Osteoporosis Screening Using Low-Dose Chest CT among Individuals Undergoing Lung Cancer Screening in a Korean Population.

Opportunistic osteoporosis screening using deep learning (DL) analysis of low-dose chest CT (LDCT) scans is a potentially promising approach for the early diagnosis of this condition. We explored bone mineral density (BMD) profiles across all adult ages and prevalence of osteoporosis using LDCT with DL in a Korean population. This retrospective study included 1915 participants from two hospitals who underwent LDCT during general health checkups between 2018 and 2021.

Deep learning-based respiratory muscle segmentation as a potential imaging biomarker for respiratory function assessment.

Respiratory diseases significantly affect respiratory function, making them a considerable contributor to global mortality. The respiratory muscles play an important role in disease prognosis; as such, quantitative analysis of the respiratory muscles is crucial to assess the status of the respiratory system and the quality of life in patients. In this study, we aimed to develop an automated approach for the segmentation and classification of three types of respiratory muscles from computed tomography (CT) images using artificial intelligence.

Assessing lung fluid status using noninvasive bioelectrical impedance analysis in patients with acute heart failure: A pilot study.

Background: Outpatient monitoring of pulmonary congestion in heart failure (HF) patients may reduce hospitalization rates. This study tested the feasibility of non-invasive high-frequency bioelectrical impedance analysis (HF-BIA) for estimating lung fluid status.

Methods: This prospective study included 70 participants: 50 with acute HF (HF group) and 20 without HF (control group).

Is a Camera-Type Portable X-Ray Device Clinically Feasible in Chest Imaging?: Image Quality Comparison with Chest Radiographs Taken with Traditional Mobile Digital X-Ray Devices.

Purpose: To evaluate whether the image quality of chest radiographs obtained using a camera-type portable X-ray device is appropriate for clinical practice by comparing them with traditional mobile digital X-ray devices.

Materials And Methods: Eighty-six patients who visited our emergency department and underwent endotracheal intubation, central venous catheterization, or nasogastric tube insertion were included in the study. Two radiologists scored images captured with traditional mobile devices before insertion and those captured with camera-type devices after insertion.

Clinical validation of a deep-learning-based bone age software in healthy Korean children.

Purpose: Bone age (BA) is needed to assess developmental status and growth disorders. We evaluated the clinical performance of a deep-learning-based BA software to estimate the chronological age (CA) of healthy Korean children.

Methods: This retrospective study included 371 healthy children (217 boys, 154 girls), aged between 4 and 17 years, who visited the Department of Pediatrics for health check-ups between January 2017 and December 2018.

Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening.

To evaluate diagnostic efficacy of deep learning (DL)-based automated bone mineral density (BMD) measurement for opportunistic screening of osteoporosis with routine computed tomography (CT) scans. A DL-based automated quantitative computed tomography (DL-QCT) solution was evaluated with 112 routine clinical CT scans from 84 patients who underwent either chest (N:39), lumbar spine (N:34), or abdominal CT (N:39) scan. The automated BMD measurements (DL-BMD) on L1 and L2 vertebral bodies from DL-QCT were validated with manual BMD (m-BMD) measurement from conventional asynchronous QCT using Pearson's correlation and intraclass correlation.

Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation.

The pectoralis muscle is an important indicator of respiratory muscle function and has been linked to various parenchymal biomarkers, such as airflow limitation severity and diffusing capacity for carbon monoxide, which are widely used in diagnosing parenchymal diseases, including asthma and chronic obstructive pulmonary disease. Pectoralis muscle segmentation is a method for measuring muscle volume and mass for various applications. The segmentation method is based on deep-learning techniques that combine a muscle area detection model and a segmentation model.

Multicentre external validation of a commercial artificial intelligence software to analyse chest radiographs in health screening environments with low disease prevalence.

Objectives: To externally validate the performance of a commercial AI software program for interpreting CXRs in a large, consecutive, real-world cohort from primary healthcare centres.

Methods: A total of 3047 CXRs were collected from two primary healthcare centres, characterised by low disease prevalence, between January and December 2018. All CXRs were labelled as normal or abnormal according to CT findings.

The spectrum of magnetic resonance imaging proton density fat fraction (MRI-PDFF), magnetic resonance spectroscopy (MRS), and two different histopathologic methods (artificial intelligence pathologist) in quantifying hepatic steatosis.

Background: The grade of hepatic steatosis is assessed semi-quantitatively and graded as a discrete value. However, the proton density fat fraction (PDFF) measured by magnetic resonance imaging (MRI) and FF measured by MR spectroscopy (FF) are continuous values. Therefore, a quantitative histopathologic method may be needed.

External validation of deep learning-based bone-age software: a preliminary study with real world data.

Artificial intelligence (AI) is increasingly being used in bone-age (BA) assessment due to its complicated and lengthy nature. We aimed to evaluate the clinical performance of a commercially available deep learning (DL)-based software for BA assessment using a real-world data. From Nov.

Multifragmentary patellar fracture has a distinct fracture pattern which makes coronal split, inferior pole, or satellite fragments.

The present study aimed to map the location and frequency of fracture lines on the coronal articular and sagittal planes in multifragmentary patellar fractures. 66 multifragmentary patellar fractures were digitally reconstructed using the 3D CT mapping technique. The coronal articular surface and midsagittal fracture maps were produced by superimposing each case over a single template.

Usefulness of Pulmonary Rehabilitation in Non-Small Cell Lung Cancer Patients Based on Pulmonary Function Tests and Muscle Analysis Using Computed Tomography Images.

Purpose: The usefulness of rehabilitation in patients with reduced lung function before lung surgery remains unclear, and there is no adequate method for evaluating the effect of rehabilitation. We aimed to evaluate the usefulness of rehabilitation in patients with non-small cell lung cancer (NSCLC) undergoing lung cancer surgery.

Materials And Methods: We retrospectively analyzed the medical records of NSCLC patients at Korea University Guro Hospital between 2018 and 2020.

Attenuation profile matching: An accurate and scan parameter-robust measurement method for small airway dimensions in low-dose CT scans.

Purpose: The dimensions of small airways with an internal diameter of less than 2-3 mm are important biomarkers for the evaluation of pulmonary diseases, such as asthma and chronic obstructive pulmonary disease (COPD). The resolution limitations of CT systems, however, have remained a barrier to be of use for determining the small airway dimensions. We present a novel approach, called the attenuation profile matching (APM) method, which allows for the accurate determination of the small airway dimension while being robust to varying CT scan parameters.

Impact of image denoising on image quality, quantitative parameters and sensitivity of ultra-low-dose volume perfusion CT imaging.

Objectives: To examine the impact of denoising on ultra-low-dose volume perfusion CT (ULD-VPCT) imaging in acute stroke.

Methods: Simulated ULD-VPCT data sets at 20 % dose rate were generated from perfusion data sets of 20 patients with suspected ischemic stroke acquired at 80 kVp/180 mAs. Four data sets were generated from each ULD-VPCT data set: not-denoised (ND); denoised using spatiotemporal filter (D1); denoised using quanta-stream diffusion technique (D2); combination of both methods (D1 + D2).

Effects of radiation dose reduction in Volume Perfusion CT imaging of acute ischemic stroke.

Purpose: To examine the influence of radiation dose reduction on image quality and sensitivity of Volume Perfusion CT (VPCT) maps regarding the detection of ischemic brain lesions.

Methods And Materials: VPCT data of 20 patients with suspected ischemic stroke acquired at 80 kV and 180 mAs were included. Using realistic reduced-dose simulation, low-dose VPCT datasets with 144 mAs, 108 mAs, 72 mAs and 36 mAs (80 %, 60 %, 40 % and 20 % of the original levels) were generated, resulting in a total of 100 datasets.

Multilevel analysis of spatiotemporal association features for differentiation of tumor enhancement patterns in breast DCE-MRI.

Purpose: Analyzing spatiotemporal enhancement patterns is an important task for the differential diagnosis of breast tumors in dynamic contrast-enhanced MRI (DCE-MRI), and yet remains challenging because of complexities in analyzing the time-series of three-dimensional image data. The authors propose a novel approach to breast MRI computer-aided diagnosis (CAD) using a multilevel analysis of spatiotemporal association features for tumor enhancement patterns in DCE-MRI.

Methods: A database of 171 cases consisting of 111 malignant and 60 benign tumors was used.