AI

Catch up on the top radiology content of the past week.

The AI-powered Heuron ICH software reportedly has an 86 percent sensitivity rate for diagnosing intracranial hemorrhage on computed tomography (CT) scans.

An AI model that includes extracted radiomic features from CT scans more than doubled the sensitivity rate for preoperative prediction of lung cancer recurrence in comparison to traditional TNM staging, according to study findings to be presented at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting in Chicago.

In addition to detecting missed lung nodules on X-rays, the AI-powered Qure.ai lung cancer continuum platform reportedly automates lung nodule measurement on CT scans and facilitates multimodality reporting.

One deep learning model had a 72.4 percent accuracy rate for differentiating between benign and malignant solid pulmonary nodules on non-contrast CT while another deep learning model demonstrated an 87.1 percent AUC for differentiating benign and inflammatory findings.

Emphasizing restriction spectrum imaging (RSI), the recently launched prostate MRI software OnQ Prostate may enhance PI-RADS assessments and workflow efficiency.

Catch up on the top radiology content of the past week.

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

Image quality, sharpness, and contrast with AI-based denoising were significantly enhanced for neck CT in comparison to conventional CT image reconstruction at 100 percent and 50 percent mAs, according to newly published research.

Researchers also noted that mammography-based AI software was associated with over a threefold higher likelihood of false-positive risk scores in patients 61 to 70 years of age in comparison to women 51 to 60 years of age.

Incorporating dynamic contrast-enhanced MRI, a deep learning model demonstrated a 20 percent higher AUC in external validation testing than clinical factors alone and over a 17 percent higher AUC than radiological factors alone in predicting proliferative hepatocellular carcinoma (HCC).

Reportedly trained on thousands of computed tomography scans, the e-Lung software utilizes machine learning to detect and assess the progression of features associated with interstitial lung diseases.

An emerging deep learning algorithm had a lower AUC and sensitivity than urological radiologists for differentiating between small renal masses on computed tomography (CT) scans but had a 21 percent higher sensitivity rate than non-urological radiologists, according to new research.

The artificial intelligence (AI)-powered module provides a prostate segmentation tool for MRI-guided transurethral ultrasound ablation (TULSA) procedures in patients with prostate cancer.

Catch up on the top radiology content of the past week.

In a study involving over 1,000 visible prostate lesions on biparametric MRI, a deep learning algorithm detected 96 percent of clinically significant prostate cancer (csPCa) in comparison to a 98 percent detection rate for an expert genitourinary radiologist.

Researchers found a 98.3 percent concordance between attending radiology reports and AI assessments for possible cervical spine fractures on CT, according to new research presented at the 2024 ARRS Annual Meeting.

Reportedly validated in more than 10 clinical trials, the AngioFlow perfusion imaging software enables timely identification of brain regions with cerebral blood flow reduction and those with significant hypoperfusion.

Catch up on the top radiology content of the past week.

While noting a variety of pitfalls with the chatbot ranging from hallucinations to improper citations, the review authors found the use of ChatGPT in radiology demonstrated “high performance” in 37 out of 44 studies.

Catch up on the top radiology content of the past week.

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

An emerging deep learning radiomics model, based on B-mode ultrasound and color Doppler flow imaging, demonstrated a 91 percent AUC for predicting lymphovascular invasion in a multicenter study of women with invasive breast cancer.

The artificial intelligence (AI)-powered applications reportedly allow clinicians to diagnose pulmonary edema and measure left ventricle ejection fraction within seconds.

One of the features on the new Voluson Signature 20 and 18 ultrasound devices reportedly uses automated AI tools to facilitate a 40 percent reduction in the time it takes to perform second trimester exams.

A new study shows that GPT-4 may offer comparable error detection rates to those of attendings and radiology residents in reviewing radiology reports, but researchers noted key caveats as well.

Artificial intelligence (AI) assessment of mammography images may significantly enhance the prediction of invasive breast cancer and ductal carcinoma in situ (DCIS) in women with breast cancer, according to new research presented at the Society for Breast Imaging (SBI) conference.

For the prediction of axillary lymph node metastasis in patients with breast cancer, an MRI-based, 4D convolutional neural network model demonstrated an AUC of 87 percent and sensitivity of 89 percent, according to new research.

Catch up on the top radiology content of the past week.

The inclusion of simulated adjudication for resolving discordant nodule classifications in a deep learning model for assessing lung adenocarcinoma on chest CT resulted in a 12 percent increase in sensitivity rate.