Imaging Informatics: Page 42

How to monitor radiology AI software after installation

Algorithms often fail to meet performance claims that are derived from validation. In this article, we explore methods for monitoring the performance of AI tools in clinical use.

January 30, 2024

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4 Trends in medical imaging in 2024: Breakthroughs in radiology productivity

AI has long been a popular term in radiology, and it looks likely to continue to be a driving force in 2024. In this article, Flywheel predicts how medical imaging AI will improve radiological assessments in the coming year.

January 30, 2024

Artificial Intelligence Ai Abstract

Will new CPT codes spark utilization of quantitative MRI?

Two new CPT codes for brain MRI will track AI software analysis of the brain with comparison to prior MR studies. Plus, what brain imaging analysis is revealing about contact sports, warfighter brain, and more.

January 30, 2024

Researchers used diffusion-tensor imaging, an MRI technique, to study the impact of soccer heading on the brain. This method tracks the microscopic movement of water molecules through brain tissue to characterize the brain's microstructure. Image and caption courtesy of the RSNA.

Deep learning could improve lung ultrasound interpretation

By Amerigo Allegretto

Deep learning could improve real-time lung ultrasound interpretation.

January 30, 2024

Workflow diagram showing real-time lung ultrasound segmentation with U-Net. Images available for republishing under Creative Commons license (CC BY 4.0 DEED, Attribution 4.0 International) and courtesy of Ultrasonics.

CT radiomics predict lymph node metastasis in bladder cancer patients

By Kate Madden Yee

A deep-learning model that combines CT radiomics and clinical information shows promise for predicting lymph node metastasis in bladder cancer patients, researchers have reported.

January 29, 2024

Automated protocoling algorithm reduces radiologist CT workload

By Kate Madden Yee

An automated protocoling algorithm used with a hospital's electronic health record software reduces radiologist CT exam workload, a recent report found.

January 18, 2024

ChatGPT-4 shows improvement over GPT-3.5 in text-based imaging cases

By Amerigo Allegretto

ChatGPT-4 has greater diagnostic accuracy than ChatGPT-3.5 when assessing text-based medical imaging cases.

January 18, 2024

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MRI radiomics help predict cervical cancer treatment outcomes

By Kate Madden Yee

"Handcrafted" radiomics and deep-learning radiomics based on pretreatment MRI data could help predict locally advanced cervical cancer concurrent chemoradiotherapy treatment outcomes.

January 17, 2024

Signify Research’s medical imaging predictions for 2024

By Kelly Patrick

Signify Research has released its top 10 predictions for the medical imaging market in 2024, including five for imaging IT and AI and five for modalities.

January 16, 2024

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Reimbursement issues give impetus to AI adoption

By Sanjay M. Parekh, PhD

Adoption of AI solutions remains nascent and inconsistent. The market has yet to see widespread adoption of medical AI technologies, as Sanjay M. Parekh, PhD, explains.

January 15, 2024

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ChatGPT identifies incidental CT findings

By Amerigo Allegretto

ChatGPT-4 performs well in identifying incidental findings on CT via a process called single-shot learning.

January 11, 2024

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Radiologists in Europe ramp up testing of AI

Four out of seven commercially available AI algorithms for detecting lung nodules on x-rays performed better than human readers in a recent study.

January 11, 2024

Example chest x-rays from a public test set illustrate algorithm and reader similarities to and discrepancies from the reference standard. A specialized radiologist determined the reference standard score (0, no nodule present; 100, one or more nodules present), and algorithms and human readers provided a probability score between 0 and 100 for each patient of the likelihood that the patient was a nodule case. (A) Radiograph in a man (age, 72 years) with a nodule present (reference standard score, 100) shows a true-positive result based on the average algorithm scores. (B) Radiograph in a man (age, 68 years) without a nodule present (reference standard score, 0) shows a true-negative result based on the average algorithm scores. (C) Radiograph in a woman (age, 64 years) without a nodule present (reference standard score, 0) shows a false-positive result based on the average algorithm scores. (D) Radiograph in a man (age, 37 years) with a nodule present (reference standard score, 100) shows a false-negative result based on the average algorithm scores. Corresponding lateral images and CT scans (when available) for these patients are presented in Figure S2 (supplementary data). The images shown in this figure were part of a public subset and not part of the set on which metrics are reported, which remains confidential for reevaluation in the future. Algorithm scores provided for the images are raw, uncalibrated scores and cannot be directly compared to each other; they are provided for indicative purposes only. Image and caption courtesy of Radiology.

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