Technology

Cardiac CT

Neurology CT

Oncology CT

Women's Health CT

Facility Management

Cardiac MRI

Neurology MRI

Oncology MRI

Women's Health MRI

Mammography

Molecular Imaging

Cardiac Ultrasound

Oncology Ultrasound

Women's Health Ultrasound

Ultrasound

Orthopedic X-Ray

X-Ray

News

All News

Blogs

Media

Around the Practice 

Between the Lines

Image IQ

Medical World News

Podcasts

Rad-View

Videos

Conferences

Conference Coverage

Conference Listing

DI Executive

Resources

CME/CE

E-books

Education

Interactive Tool Kits

Partners

Subscribe

Advertise

About

Contact Us

Editorial

Editorial Board

Do Not Sell My Personal Information 

Privacy

Terms and Conditions

Diagnostic Imaging

Facebook

LinkedIn

YouTube

Spotlight

RSNA

Disease Spotlight: Prostate Cancer

Rad-View: Generic Contrast Agents 

Rad View: PSMA PET Imaging Recommendations

Unlock AI-Powered Imaging

Image IQ Quiz

Topics

View More

1rem

Topic

, Anand Narayan, M.D., and Amy Patel, M.D., offer their perspectives on the challenges faced by radiologists — who are already grappling with increasingly high volume worklists and radiologist shortages — in implementing expanded breast cancer screening as per recently updated recommendations from the 

United States Preventive Services Task Force (USPSTF)

For breast cancer risk assessments, Dr. Patel said another challenge is that not all facilities have access to high-risk breast clinics or genetics programs, limitations that often fall upon the shoulders of breast imaging centers. Dr. Patel noted that artificial intelligence (AI) and possible implementation of a risk-based model with AI may have potential for facilitating appropriate triaging of patients.

In regard to practical steps radiologists can take to help raise awareness of new recommendations to help address disparities in breast cancer screening, Dr. Patel emphasized the importance of an ongoing dialogue with primary care providers.

“Keeping the lines of communication open with your referring providers is paramount. When new recommendations come down the pike, we as a breast imaging community really need to take it upon ourselves to educate our referring providers,” noted Dr. Patel, the medical director of the Liberty Hospital Breast Care Center in Kansas City and an assistant professor at the University of Missouri-Kansas City.

(Editor’s note: For related content, see “

New Research Shows Consequences of Delayed Diagnosis in Mammography Screening

The Reading Room Podcast: Emerging Concepts in Breast Cancer Screening and Health Equity Implications, Part 2

The Reading Room Podcast: Emerging Concepts in Breast Cancer Screening and Health Equity Implications, Part 1

For more insights from Dr. Narayan and Dr. Patel, listen below or 

In the third episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss the challenges of expanded breast cancer screening amid a backdrop of radiologist shortages and ever-increasing volume on radiology worklists. 

The Reading Room Podcast: Emerging Concepts in Breast Cancer Screening and Health Equity Implications, Part 3

An artificial intelligence algorithm for dynamic contrast-enhanced breast MRI offered a 93.9 percent AUC for breast cancer detection, and a 92.3 percent sensitivity in BI-RADS 3 cases, according to new research presented at the Society for Breast Imaging (SBI) conference.

Emerging AI Algorithm Shows Promise for Abbreviated Breast MRI in Multicenter Study

Offering expedited calculation of prostate volume, Clarius Prostate AI is reportedly the first AI-enabled prostate measurement tool to garner FDA clearance for use with handheld ultrasound.

FDA Clears AI-Powered Prostate Volume Measurement for Handheld Ultrasound

Employing deep learning image reconstruction, parallel imaging and multi-slice acceleration in a sub-five-minute 3T knee MRI, researchers noted 100 percent sensitivity and 99 percent specificity for anterior cruciate ligament (ACL) tears.

Could AI-Powered Abbreviated MRI Reinvent Detection for Structural Abnormalities of the Knee? 

The T-Mode Anterior Knee feature reportedly offers a combination of automated segmentation and real-time conversion of grayscale ultrasound images into color-coded visuals that bolster understanding for novice ultrasound users.

Clarius Mobile Health Unveils Anterior Knee Feature for Handheld Ultrasound

In comparison to native 64-mT MRI, the deep learning generative model LowGAN offered enhanced white matter lesion conspicuity and image quality in a study involving patients with multiple sclerosis.

Can Deep Learning Enhance Low-Field MRI for Multiple Sclerosis Assessment?

While there has been a fair amount of research examining the impact of artificial intelligence (AI) with two-dimensional mammography, there has been relatively limited data on the use of AI with digital breast tomosynthesis (DBT), noted Manisha Bahl, M.D., MPH, FSBI, in a recent interview.

Dr. Bahl also noted there has been little data in the AI computer-assisted diagnosis (CAD) breast imaging research with respect to what specific breast cancers are detected or not detected with AI.

“The purpose of our (new) study was to fill that gap in knowledge or understanding by providing an analysis of the types of breast cancers that are missed versus detected by a commercial AI based CAD algorithm for tomosynthesis,” explained Dr. Bahl, an associate professor of radiology at Harvard Medical School.

For the study, which was recently presented at the Society for Breast Imaging (SBI) conference, Dr. Bahl and colleagues assessed the Genius AI Detection 2.0 software (Hologic) in 5,000 patients who had DBT screening exams. They found that the AI software detected and correctly localized 90 percent of true-positive cases.

Dr. Bahl noted the AI software was more likely to detect invasive ductal carcinoma and lymph node positive carcinoma. The AI software was less likely to detect true positive cancers that presented as asymmetries on mammography as well as invasive lobular carcinoma, according to Dr. Bahl.

“These results suggest that the AI algorithm is optimized to detect clinically significant lymph node positive cancers (and) may require further training to identify invasive lobular cancers, which are known to be difficult to detect because of their unique infiltrative growth pattern,” posited Dr. Bahl, the director of the breast imaging fellowship program at Massachusetts General Hospital.

Dr. Bahl also said she was impressed by the ability of the AI software to detect 32 percent of the false negative cases in the study.

“I think those results suggest that integrating AI into our practice could potentially help decrease the false negative rate of screening tomosynthesis, which is an important metric, an important surrogate marker for long term outcomes. Those results are very promising to me and show that AI has the potential to improve our quality in addition to our efficiency,” noted Dr. Bahl.

Multicenter Mammography Study Shows Greater than 10 Percent Increase in Breast Cancer Detection with Adjunctive AI

New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection

Can AI Bolster Breast Cancer Detection in DBT Screening?

For more insights from Dr. Bahl, watch the video below.

In a recent interview, Manisha Bahl, MD, discussed new research looking at the impact of AI in digital breast tomosynthesis (DBT) screening for breast cancer.

What New Research Reveals About the Impact of AI and DBT Screening: An Interview with Manisha Bahl, MD

In a recent interview, Manisha Bahl, M.D., discussed key findings from a new study on AI and digital breast tomosynthesis that she presented at the Society for Breast Imaging (SBI) conference.

There is significant variability with respect to predicting postoperative recurrence risk in pediatric patients who undergo surgery for gliomas, which contributes to considerable burden and stress for patients and their families. With this in mind, Benjamin Kann, M.D., and his colleagues developed a temporal deep learning model that evaluates longitudinal magnetic resonance imaging (MRI) to ascertain the risk of pediatric glioma recurrence.

In a new study, published earlier today in 

, Dr. Kann and his colleagues found that the model’s average area under the receiver operating characteristic curve (AUROC) for predicting one-year event-free survival (EFS) with longitudinal MRI assessment was 80.6 percent in contrast to 50.3 percent for single MRI scan assessment.

In a recent interview, Dr. Kann said the temporal deep learning model may provide a viable option for synthesizing current and previous imaging at the point of care.

“The point is to really take that historical imaging and analyze it all together to provide the most accurate prediction of… what will happen within the next year for this patient,” added Dr. Kann, an assistant professor of radiation oncology at Harvard Medical School.

While emphasizing that subsequent prospective research will be necessary to validate the study findings, Dr. Kann noted the potential of the temporal deep learning model in facilitating earlier treatment interventions for patients deemed to have high recurrence risk and perhaps longer intervals between follow-up MRI scans for patients identified as having a lower risk of glioma recurrence.

Dr. Kann also suggested broader implications with the study perhaps serving as a springboard to more investigations with AI assessment of longitudinal imaging versus single scans.

“I think there's been a lot done at single time point analyzes, and for a long time, it's been seen as kind of unreachable or infeasible to do these types of longitudinal medical imaging analyzes. … We hope that we've shown that there is a path forward there, and (we’re) just excited to see other groups do even more interesting and innovative things inspired by our work,” noted Dr. Kann, a radiation oncologist affiliated with the Brigham and Women’s Hospital and the Dana-Farber Cancer Institute in Boston.

Emerging PET Agent Gets FDA Fast Track Designation for Glioma Imaging

A Closer Look at MRI-Guided Adaptive Radiotherapy for Monitoring and Treating Glioblastomas

Is MRI Contrast Enhancement Necessary for Long-Term Monitoring of Diffuse Glioma?

For more insights from Dr. Kann, watch the video below.

In a recent interview, Benjamin Kann, MD, discusses the use of AI evaluation of longitudinal MRI scans to improve the prediction of postoperative glioma recurrence in pediatric patients.

Can AI Assessment of Longitudinal MRI Scans Improve Prediction for Pediatric Glioma Recurrence?

In a recent interview, Benjamin Kann, M.D., discussed the use of an emerging AI model that can assess longitudinal MR imaging to help predict postoperative recurrence risk for gliomas in pediatric patients.

The quantitative assessments provided by image-based artificial intelligence (AI) models in mammography screening may have a significant impact in ascertaining short-term breast cancer risk, according to new research presented at the European Congress of Radiology (ECR) conference.

In a study involving over 8,000 women (including 780 women with incident breast cancer), researchers found that AI-based risk models (ProFound AI Risk, iCAD) for digital mammography and digital breast tomosynthesis (DBT) predicted 40 percent and 48 percent, respectively, of future breast cancers in high-risk women.

“Another important thing is this (research was focused on) short-term risk, so this is really important to guide the choice of precision screening for women. Who needs what? How often? … How can we really use this information, this risk based on the image qualities, to drive precision screening better than we're doing it right now?,” posited lead study author Emily Conant, M.D., a professor emeritus in the Department of Radiology at the Hospital of the University of Pennsylvania.

In a recent interview, Dr. Conant also discussed another study presented at the ECR conference that examined the use of an algorithm to facilitate detection of breast arterial calcification (BAC) in mammograms. Dr. Conant and her colleagues found that the algorithm achieved a 98 percent case-level AUC for BAC detection.

“I think the fact that the mammogram shows more than just, you know, breast cancer and risk for future breast cancer, short-term risk, long-term risk, but can also help sort of triage women who are at higher cardiac risk. That's really huge,” maintained Dr. Conant. “Again, we need more studies, we need more data, we need prospective studies and all of that. But this is a rapidly expanding area that I think really adds benefit and worth, etc, to mammographic screening.”

Study: Mammography AI Leads to 29 Percent Increase in Breast Cancer Detection

Mammography Study Shows Merits of AI for Improving Breast Cancer Detection and Effectiveness of Recalls

Study Shows Link Between Breast Arterial Calcification and Significant Cardiovascular Risks

For more insights from Dr. Conant, watch the video below.

Emily Conant, MD, discusses new research, presented at the European Congress of Radiology (ECR) conference, that explores the use of image-based AI risk models and breast arterial calcification detection in mammography screening.

New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection 

In a recent interview, Emily Conant, M.D., discussed findings from two studies presented at the European Congress of Radiology (ECR) conference that showed the potential impact of image-based AI risk models and breast arterial calcification detection in mammography screening.

While single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is well validated for the detection of coronary artery disease (CAD) and other cardiovascular abnormalities, there is a degrading effect with attenuation that requires an additional computed tomography (CT) scan to address attenuation compensation.

However, in a recent interview, Abhinav Kumar Jha, Ph.D, noted there are a large number of SPECT scanners that do not have the CT component and this issue is particularly prevalent in community hospitals and rural facilities. Dr. Jha says this problem is compounded as studies have demonstrated a higher prevalence of heart disease in rural health-care settings.

Dr. Jha pointed out that 75 percent of SPECT MPI exams are performed without attenuation compensation and cites a survey of nuclear cardiology providers that found only 5.6 percent provided CT-based attenuation correction.

“Therefore, you can imagine there's a very important need for a large segment of the patient population to have access to this technology of attenuation correction without being able to take a CT scan,” noted Dr. Jha, an associate professor of biomedical engineering and radiology at the Washington University School of Medicine in St. Louis.

However, in recently published research, Dr. Jha and other researchers developed a deep learning technique that provides CT-less (dubbed CTLESS) attenuation compensation. With this research and other studies, Dr. Jha said the CTLESS approach has provided consistent performance across scanner models and patients with different degrees of heart disease.

While acknowledging that more studies are needed to validate the technology, Dr. Jha noted the CTLESS approach has potential to bolster access to SPECT MPI.

“I'm excited about the CTLESS technology (and) making the process of attenuation compensation more accessible, affordable as well as safe,” emphasized Dr. Jha.

Assessing MACE Risk in Women: Can an Emerging Model with SPECT MPI Imaging Have an Impact?

Radiology ‘Game-Changer’: FDA Approves PET Agent for Enhanced Detection of Coronary Artery Disease

Study Shows Merits of CTA-Derived Quantitative Flow Ratio in Predicting MACE

For more insights from Dr. Jha, watch the video below.

Abhinav K. Jha, Ph.D., discusses emerging research for a deep learning CT-less alternative for providing attenuation compensation for SPECT MPI.

Can Deep Learning Provide a CT-Less Alternative for Attenuation Compensation with SPECT MPI?

In a recent interview, Abhinav K. Jha, Ph.D., discussed key challenges with the use of SPECT MRI and how an emerging deep learning model may facilitate attenuation compensation without the need for an additional computed tomography (CT) scan.

For busy neuroradiology departments, adjunctive artificial intelligence (AI) may facilitate timely detection and triage of computed tomography (CT) scans showing subdural hematomas (SDHs).

In a new retrospective multicenter study presented at the International Stroke Conference (ISC), researchers compared the AI-powered Rapid SDH (RapidAI) software versus three expert neuroradiologist assessments of non-contrast-enhanced head CTs in 313 patients (mean age of 63) for the detection of SDH.

The researchers found that the AI software provided a 92.4 percent sensitivity and a 98.7 percent specificity for subdural hematomas. Specifically, the Rapid SDH software offered a 91.5 percent sensitivity for chronic SDH and a 92.7 percent sensitivity for acute/subacute SDH, according to the research.

In a recent interview from the ISC, lead study author Jeremy Heit, M.D., Ph.D., spoke about the potential impact of the research.

“Those are really good numbers. This is a hard problem, and as one of the readers, some of these hemorrhages were really subtle,” noted Dr. Heit, the chief of neuroimaging and neurointervention at the Stanford University School of Medicine. “So the fact that they're being picked up, I actually think that's really impressive because it's not a trivial task for a computer to be able to deal with.”

Bringing up an example of multiple traumas requiring a number of acute imaging scans in an emergency department, Dr. Heit said the accuracy and efficiency of the Rapid SDH software can have a significant impact in triaging important cases.

“I think it's really going to be helpful to have it work that quickly. It's a three-minute processing (time) to be able to flag something and bump it up on your queue. That is super helpful,” maintained Dr. Heit.

FDA Clears AI-Powered Module for Detecting Hemispheric Subdural Hematomas on CT

Key Findings on Non-Contrast CT Signal Higher Complication Risks in Patients with Intracerebral Hemorrhage

FDA Clears New AI Tool for Diagnosing Subdural Hemorrhage

For more insights from Dr. Heit, watch the video below.

Neuroradiologist Jeremy Heit, MD, discussed new research showing the efficacy of the AI-powered software Rapid SDH for detecting subdural hematomas on non-contrast head CT scans.

Employing AI in Detecting Subdural Hematomas on Head CTs: An Interview with Jeremy Heit, MD, PhD

1. Heit J. Performance of Rapid SDH for detection of both acute and chronic subdural hematomas. Presented at the International Stroke Conference, February 4-7, 2025, Los Angeles. For more info, visit 

https://professional.heart.org/en/meetings/international-stroke-conference

In a video interview from the International Stroke Conference (ISC), Jeremy Heit, M.D., Ph.D., discussed new research revealing over 90 percent sensitivity and specificity rates for AI detection of subdural hematomas on non-contrast-enhanced head CTs.

Increased transparency is a cornerstone of the recent 

draft guidance from the Food and Drug Administration (FDA) on submissions for AI-enabled software

. While this will result in an increased amount of documentation on the training as well as limitations for adjunctive use of AI models, Nina Kottler, M.D., maintained in a recent interview that this well-intended effort at transparency may fall short of having an impact in daily practice.

“ … Any of this documentation that the FDA is requiring right now is not so helpful at the point of care. It is good that they're doing it. It's not a bad thing. It's a very good thing, but we need a way now to translate that information to be helpful at the point of care,” posited Dr. Kottler, the associate chief medical officer of clinical AI at Radiology Partners.

Whether it comes in the form of heat mapping or a color-coded assessment by the adjunctive AI, Dr. Kottler says the capability of the AI tool to express a degree of confidence in the evaluation can be beneficial for end-user radiologists.

Additionally, Dr. Kottler maintained that there should be clear transparency on how the adjunctive AI is making its assessment. She cautioned that most people don’t recognize that adjunctive AI doesn’t consider every single piece of input data in its interpretation.

“ … Another thing that the FDA should be requiring is to tell the end user, what are the images, or what are the components of the language, or whatever that the AI is using so radiologists know when they're looking at a study, ‘Oh, this series wasn't even evaluated by the AI.’ That is really important information,” emphasized Dr. Kottler.

How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 1

Study Assesses Lung CT-Based AI Models for Predicting Interstitial Lung Abnormality

For more insights from Dr. Kottler, watch the video below.

Nina Kottler, MD, discusses the increased transparency emphasis of the recent FDA guidance on AI-enabled software and the potential impact upon radiologists.

How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 2

In the second part of a two-part interview, Nina Kottler, M.D., says the transparency emphasis of the recent FDA guidance on AI-enabled software is welcome but needs to go beyond additional documentation to clarify how adjunctive AI is making its decisions.

The recently issued draft guidance from the Food and Drug Administration (FDA) on submissions for AI-enabled software reflects more of an emphasis on the total product life cycle for AI products, according to Nina Kottler, M.D., the Associate Chief Medical Officer of Clinical AI for Radiology Partners.

Accordingly, in a recent interview, Dr. Kottler noted that a key cornerstone of the 

 is continuous monitoring of AI software, which may be challenging for smaller AI developers in radiology. She suggested that perhaps an independent group could assist with continuous monitoring of AI software.

“ … What if we had a separate group that could not only help out the AI vendors that maybe couldn't afford to do it, or don't have access to all the data, or (are) not mature enough yet to be there?” posited Dr. Kottler, the Associate Chief Medical Officer of Clinical AI for Radiology Partners.

While Dr. Kottler said the FDA guidance’s increased emphasis on transparency of the training and development of AI software is an important priority, the resulting information needs to be conveyed in a practical way for end-user radiologists.

“Now as an end user, as I'm going through my day looking at 100 cases or 200 cases, many of which are using AI, am I going to that document to look and say, ‘Is this patient and is this exam something that (the AI software) was very specifically trained on? No, that's not realistic. So how do you translate these guidelines into something that's more realistic for the end user? That's going to be the important component. That's the part that I really care about,” maintained Dr. Kottler.

Current and Emerging Insights on AI in Breast Imaging: An Interview with Mark Traill, MD, Part 3

Diagnostic Imaging’s Top Five AI Content of 2024

Nina Kottler, MD, MS on Adoption and Impact of AI in Radiology

Nina Kottler, MD discusses the recently issued FDA guidance on AI-enabled devices and the potential impact for AI developers in radiology.

How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 1 

In the first part of a two-part interview, Nina Kottler, M.D. offers insights and perspective on the recently issued guidance from the FDA on AI-enabled devices and how it may impact developers in the radiology field.

While vRad’s teleradiology breast imaging service provides remote interpretation of nearly 250,000 screening and diagnostic exams annually, Arlene Sussman, M.D., says the organization emphasizes a personalized “boutique service.”

In a recent interview, Dr. Sussman said live video streaming with very large cameras connects participating radiology providers with vRad’s remote subspecialist breast radiologists, facilitating real-time communication with technologists, referrers, and their patients. Dr. Sussman added that vRad’s 11 subspecialist breast radiologists are assigned consistent coverage of certain participating sites, enabling another level of personalized care.

“For example, I cover certain sites Mondays from eight to four. (On that day), the cases come to me. I’m the only one. They get to know me. I get to know their referrers, their patients, and the technologists. I become part of their team that day and it doesn’t vary,” emphasized Dr. Sussman, the medical director and clinical chief of women’s imaging at vRad (Virtual Radiologic).

Noting that there may be upward of 1,500 cases on a worklist at any given point in time, Dr. Sussman is a strong advocate of artificial intelligence (AI) for facilitating timely triage as well as quality metrics in mammography interpretation. She is also enthusiastic about the potential of AI incorporating prior exam findings.

“ … They say diamonds are a girl's best friend. Well, they're right, but so are prior films for comparison. If you had something that has been there for four years and it's stable by definition, it is benign, and AI doesn't know that, because it only looks at the current study. But if you had AI for the prior and then AI for the current (study), imagine how powerful that could be,” posited Dr. Sussman.

Dr. Sussman added that another powerful benefit of teleradiology is reducing disparities in access to breast imaging, a key driver for vRad’s breast imaging service since its inception.

“Our first goal was to reach patients who had limited or no access to subspecialists, and there was no reason why they should be shortchanged. That's where we started, very humbly going out to small rural practices that didn't have subspecialists, and we manned them remotely, and then we never looked back,” noted Dr. Sussman.

Can an Emerging AI Software for DBT Help Reduce Disparities in Breast Cancer Screening?

Can an Emerging AI Model Enhance Early Breast Cancer Detection on MRI?

For more insights from Dr. Sussman, watch the video below.

In a recent interview, Arlene Sussman, M.D., discussed her experience in leading vRad’s teleradiology breast imaging service and facilitating personalized care, utilizing AI and promoting health equity.

Teleradiology and Breast Imaging: Keys to Facilitating Personalized Service, Efficiency and Equity

In a recent interview, Arlene Sussman, M.D., discussed her experience in leading vRad’s teleradiology breast imaging service, how to foster personalized care in breast cancer screening, utilizing AI to help mitigate daunting worklists and improving access to subspecialty care.

For many decades, interventionalists had to rely upon two-dimensional X-ray views to guide devices through blood vessels but the emergence of Fiber Optic RealShape (FORS) technology may enhance the ease and efficiency with which complex aortic procedures are performed.

In an interview, Atul Gupta, M.D., noted the FORS technology (Philips), which provides 3D light-based imaging of blood vessels as opposed to two-dimensional X-ray imaging, has led to a 37 percent reduction in procedure time and a 56 percent reduction in radiation exposure for complex aortic procedures at one facility as per a recent study published in the 

Dr. Gupta said the recently launched LumiGuide (Philips) takes FORS technology a step further. LumiGuide offers artificial intelligence (AI)-based recognition, which facilitates efficient registration of guidewires as well as enhanced accuracy, and 3D Hub technology that enables visualization of any catheter in 3D and without radiation, even from angles that one can’t physically achieve with a standard C-arm device, noted Dr. Gupta, the chief medical officer for Image-Guided Therapy and Precision Diagnosis at Philips.

Study Examines Photon-Counting CT for CAD Detection in Patients Having TAVR Procedures

Key Takeaways from Multidisciplinary Roundtable on Intravascular Ultrasound Use for Peripheral Vascular Interventions

In a recent interview, Atul Gupta, M.D. discussed the evolution and potential impact of Fiber Optic RealShape (FORS) technology, which uses light instead of radiation to facilitate real-time 3D imaging that may lead to significant efficiencies with complex aortic procedures.

Technology

Latest News