Download Mobile App

Video Library

AI Reduces CT Turnaround and Wait Times for Positive Pulmonary Embolus

By MedImaging International staff writers
Posted on 10 Apr 2023

Image: AI has been shown to cut CT turnaround and wait times for positive pulmonary embolus (Photo courtesy of Freepik)

In individuals experiencing an acute pulmonary embolism (PE), prompt treatment (such as starting anticoagulation) is crucial for improving patient outcomes. A study was carried out to assess the impact of using artificial intelligence (AI)-based radiologist worklist reprioritization on the report turnaround times for CT pulmonary angiography (CTPA) examinations that were positive for acute PE. The results indicated that utilizing AI-driven worklist reprioritization led to shorter report turnaround time and waiting periods for PE-positive CTPA examinations.

The study conducted by researchers at the University of Texas Southwestern Medical Center (UTSW, Dallas, TX, USA) involved patients who had undergone CTPA in two periods: before (October 1, 2018–March 31, 2019) and after (October 1, 2019–March 31, 2020) the implementation of an FDA-approved AI tool. This tool was designed to reprioritize CTPA examinations at the top of radiologists' reading lists when acute PE was identified. By analyzing timestamps from electronic medical records and dictation systems, the researchers determined three durations: the waiting period (from examination completion to report initiation), reading time (from report initiation to availability), and overall report turnaround time (the sum of waiting and reading times). The team then compared the durations for PE-positive reports, using final imaging as a reference, between the two periods.

The research discovered that the AI-powered worklist reorganization tool was linked to considerably reduced report turnaround (47.6 vs. 59.9 minutes) and wait times (21.4 vs. 33.4 minutes) for CTPA reports indicating acute PE. However, there was no significant difference in reading times (26.3 vs. 26.5 minutes) for CTPA reports positive for acute PE when compared to reports prior to AI implementation. From these results, the investigators deduced that the AI tool could potentially facilitate quicker diagnoses by radiologists, ultimately leading to earlier interventions for acute PE cases.

Related Links:
UTSW

Visit expo >

Digital Intelligent Ferromagnetic Detector

Digital Ferromagnetic Detector

Digital X-Ray Detector Panel

Acuity G4

Biopsy Software

Affirm® Contrast

Breast Localization System

MAMMOREP LOOP

Latest General/Advanced Imaging News

Channels

Radiography

view channel

Images in a 75-year-old woman who underwent routine screening mammography in 2022. Left mediolateral oblique views shown from prior screening mammograms: (A) 2015, (B) 2018, (C) 2019, and (D) 2021. In (E) 2022, a new mass developed in the left breast at the 6-o’clock position (arrow), with a (F) corresponding irregular mass at US (arrow). Subsequent US-guided core needle biopsy yielded invasive ductal carcinoma, grade 2. The deep learning 5-year risk scores gradually increased from 2.0 (2015) to 2.1 (2018), 3.4 (2019), 3.6 (2021), and 15.3 (2022). CMFN = centimeters from nipple, LT = left, TRANS = transverse (Photo courtesy of Radiological Society of North America)

AI Tool Predicts Five-Year Breast Cancer Risk from Mammograms

Breast cancer risk assessment during routine screening is difficult because many women who develop the disease have no known genetic mutations or family history. Static risk tools provide limited discrimination... Read more

Image: Screening mammograms and artificial intelligence (AI) score changes over time in two individuals with screen-detected cancer. Full-field digital mammograms show craniocaudal (top) and mediolateral (bottom) views of the left and right breast (Photo courtesy of Radiological Society of North America)

AI Mammography Tools Detect Early Breast Cancer Signs Years Before Diagnosis

Breast cancer screening aims to detect tumors before symptoms develop, but subtle mammographic changes can appear years before diagnosis and may be missed during routine reads. Delayed detection can lead... Read more

$Image: Kyushu University researchers use Dynamic Chest Radiography (DCR) to evaluate the severity of Pulmonary Regurgitation (PR), a common complication after treatment of the congenital heart disease Tetralogy of Fallot. The analysis only requires the patient to hold their breath for seven seconds for a sequence of images of their heart to be taken. The images are then analyzed and extracted into a waveform that can show the severity of PR. The new technique\\\'s detection accuracy of severe PR is 93%, with a sensitivity of 93% and a specificity of 94%. (Photo courtesty of Kyushu University/Yuzo Yamasaki)$

Rapid X-Ray Test Quantifies Pulmonary Regurgitation After Tetralogy of Fallot Repair

Tetralogy of Fallot is the most common cyanotic congenital heart defect and can leave patients with pulmonary valve regurgitation, a backward flow of blood into the right ventricle after repair.... Read more

Image credit: Shu-Han Chen / St. Paul’s Hospital / National Taiwan University

AI Tool Flags Osteoporosis Risk from Routine Chest X-Rays

Osteoporosis is a progressive loss of bone density that is often silent until a fracture occurs. Current screening frameworks concentrate on older women and select high-risk groups. Many men, younger adults,... Read more

MRI

view channel

International Study Assesses AI for Prostate Cancer MRI Interpretation

Prostate cancer is a leading cause of cancer morbidity in men, and accurate early diagnosis hinges on expert interpretation of prostate magnetic resonance imaging (MRI). Rapid adoption of MRI-first pathways... Read more

$Image: By training the AI with simulations, researchers can reconstruct brain images with high precision using only one-tenth of the data typically required in magnetic resonance imaging. The image shows a metric known as “fractional anisotropy”—an index of myelination—obtained using conventional methods and with the new approach (Photo courtesy of Instituto de Neurociencias UMH CSIC)$

Ultrasound

view channel

$Image: A cross-section through the brain reveals the symmetric architecture of the vascular network (cyan-green) and the tracer highlighting the glymphatic system (red-yellow) along the brain\'s surface and drainage routes—imaged through the intact skull with 3D-PAULM. (Photo courtesy of Duke University)$