AI

Three-dimensional interactive heart models created with AI-enabled segmentation of CT scans may reduce ventricular tachycardia ablation procedure times by 60 percent.

Incorporating an artificial intelligence (AI) platform into the radiology workflow at a stroke management-accredited hospital may lead to projected savings of 78 days in triage time and 41 days in reporting time for radiologists over a five-year period, according to new research.

In a comparison of image-to-text large language models (LLMs), ChatGPT 4.0 offered a 95 percent sensitivity rate and an 83 percent AUC that were comparable to that of two senior radiologists and one junior radiologist interacting with LLM to differentiate between malignant and benign thyroid nodules on ultrasound.

The newly launched Progressive Loading feature, available through RamSoft’s OmegaAI software, reportedly offers radiologist rapid uploading of images that is faster than on-site networks and other cloud-based systems regardless of the network radiologists are using.

While adjunctive use of AI led to significantly higher specificity and accuracy rates in detecting cancer on breast ultrasound exams in comparison to unassisted reading by breast radiologists, researchers noted that 12 of 13 BI-RADS 3 lesions upgraded by AI were ultimately benign, according to research presented at the European Congress of Radiology.

In comparison to standard-dose lung CT, the combination of deep learning image reconstruction with ultra-low-dose CT offered similar detection and characterization of pulmonary nodules at a nearly 93 percent reduction of radiation dosing, according to new research.

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Based on findings from a multicenter study of over 1,600 patients, researchers at the European Congress of Radiology suggest the inclusion of autonomous artificial intelligence (AI) triage could facilitate up to a 75 percent reduction in prostate MRI reading workload.

Designed for full-body imaging, the new modality reportedly offers a lightweight portable design, AI-powered tools and enhanced high-resolution images.

Catch up on the most-well viewed radiology content in February 2024.

Catch up on the top AI-related news and research in radiology over the past month.

Clinicians can bill Category 1 CPT Code 75580 for adjunctive use of the artificial intelligence (AI)-powered Cleerly Ischemia software for fractional flow reserve estimates based on coronary computed tomography angiography (CCTA) scans.

For patients with suspected acute stroke, researchers noted higher image quality for deep learning-accelerated MRI, which can be completed over 11 minutes sooner than similar sequences for conventional MRI.

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What does the current evidence reveal for four FDA-cleared AI software modalities for breast ultrasound? Leading researchers review the study findings in a recently published literature review.

Offering rapid automated segmentation of brain structures, the ClearPoint 2.2 software reportedly enhances visualization for a variety of targeted, MRI-guided procedures.

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The adjunctive use of AI software led to 16 percent and 10 percent increases in sensitivity rates for the detection of ARIA-E and ARIA-H, respectively, in patients with Alzheimer’s disease, according to newly published research.

The AI-enabled HealthFLD software demonstrated a 77.8 percent sensitivity rate and a 93.2 percent specificity rate for diagnosing moderate hepatic steatosis on contrast-enhanced CT scans in a recent study of over 2,900 patients.

Facilitating timely diagnosis and intervention for brain bleeds, the artificial intelligence (AI)-powered Viz ICH Plus software may provide quantified assessment for intracranial hyperdensities, lateral ventricles and midline shifts.

The use of multiple artificial intelligence (AI) modalities to assess blood vessel density reduction and computed tomography perfusion (CTP) mismatch values facilitated a 93.3 percent detection rate for medium vessel occlusions, according to research presented recently at the International Stroke Conference.

With reported increases in tuberculosis cases after the COVID-19 pandemic, the qSpot-TB adjunctive artificial intelligence (AI) device may facilitate improved diagnosis of the disease on chest X-rays.

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Combining advanced volumetric imaging technology with deep learning, the Genius Digital Diagnostics System has reportedly shown a 28 percent reduction in false negatives for high-grade squamous intraepithelial lesions and other severe lesions in comparison to microscopic review.

Researchers found the use of deep learning reconstruction for computed tomography colonography (CTC) led to 25 percent higher subjective image quality scoring than iterative image reconstruction at a substantially reduced rate of radiation dosing.