RSNA 2017 Artificial Intelligence Preview

Road to RSNA 2017: Artificial Intelligence Preview

By Erik L. Ridley, staff writer
November 6, 2017

Our next destination on the Road to RSNA is a stop in artificial intelligence (AI) for a preview of this year's presentations on AI in medical imaging, including machine learning, deep learning, and computer-aided detection/diagnosis (CAD/CADx).

If you thought AI had taken off at RSNA 2016, you haven't seen anything yet. RSNA 2017 will feature more than 100 presentations in the seven dedicated sessions focused on AI, as well as throughout the scientific program. What's more, AI will also be featured in a bevy of hot topic sessions, refresher courses, scientific posters, and educational exhibits.

A key trend will be the use of AI techniques along with quantitative features of images (i.e., radiomics) to gain new insights on diseases. For example, presenters will discuss how AI can exploit tumor imaging features to predict the survival of patients with metastatic colon cancer, bladder cancer, or gliomas. AI algorithms were also found to be useful for assessing the malignancy risk of lung nodules on low-dose chest CT and predicting the invasiveness of lung adenocarcinoma.

Other scientific sessions will describe how AI can improve the performance and utility of current imaging modalities, including assessing cardiovascular risk on routine chest CT exams, improving the specificity of coronary CT angiography for functionally significant stenosis, and providing real-time coronary artery calcium scoring. In addition, AI can also help find lung nodules on chest x-rays and aid in the challenging diagnosis of Crohn's disease.

A number of studies will explore the potential of AI for enhancing the care of acute ischemic stroke, including determining the onset of stroke symptoms and guiding treatment decisions. Those who travel to the Windy City will also be able to learn how AI can offer value by identifying imaging cases that need priority review by a radiologist, such as patients with large pneumothoraces or malpositioned feeding tubes.

The "black box" nature of AI is often cited as a barrier to implementation in radiology. Notably, a few presentations at McCormick Place will explain how AI algorithms can generate "heat maps" that can show radiologists the areas of an image that led to its diagnosis or findings.

See below for our previews of select AI-related scientific sessions at RSNA 2017. Of course, these are just a sample of the AI content planned for the meeting. For more information on those talks, as well as other abstracts in this year's scientific and educational program, click here.

If you haven't already, please also check out our related Road to RSNA Imaging Informatics Preview for our coverage of PACS, cybersecurity, clinical decision-support, radiation dose-monitoring software, structured reporting, analytics, and issues regarding patient access to radiology results. Our Advanced Visualization section on November 8 will highlight 3D and augmented/virtual reality topics, while IT talks related specifically to women's imaging topics will be featured in our upcoming Women's Informatics Preview.

Scientific and Educational Presentations
AI can predict invasiveness of lung adenocarcinoma
Sunday, November 26 | 10:45 a.m.-10:55 a.m. | SSA05-01 | Room S404CD
A Japanese group has found that an artificial intelligence (AI) algorithm can predict the level of pathological invasiveness of lung adenocarcinoma nearly as accurately as a highly experienced radiologist.
Deep learning can assess malignancy risk of lung nodules
Sunday, November 26 | 10:55 a.m.-11:05 a.m. | SSA12-02 | Room S403A
In this scientific session, researchers will describe how a deep-learning algorithm can provide an objective measure for assessing the malignancy risk of a lung nodule.
Chest x-ray algorithm shows why it made its diagnosis
Sunday, November 26 | 11:35 a.m.-11:45 a.m. | SSA12-06 | Room S403A
A team from India will present a deep-learning algorithm that highlights the areas of the chest x-ray that led to the algorithm's diagnosis.
Machine learning may enhance utility of FFR-CT
Sunday, November 26 | 11:45 a.m.-11:55 a.m. | SSA04-07 | Room S504AB
A machine-learning algorithm shows potential for facilitating the use of fractional flow reserve (FFR) calculations on coronary CT angiography studies, according to research being presented in this scientific session.
Deep learning can identify malpositioned feeding tubes
Sunday, November 26 | 11:45 a.m.-11:55 a.m. | SSA12-07 | Room S403A
In this talk, researchers will highlight the potential of deep learning for speeding up the detection of malpositioned feeding tubes in critically ill patients.
AI detects large pneumothoraces on chest x-ray
Sunday, November 26 | 12:05 p.m.-12:15 p.m. | SSA12-09 | Room S403A
Artificial intelligence (AI) algorithms can automatically detect large pneumothoraces on chest x-ray -- potentially speeding up detection and reporting of these critical findings, according to researchers from Philadelphia.
Deep learning helps find lung nodules on chest x-ray
Monday, November 27 | 11:00 a.m.-11:10 a.m. | SSC03-04 | Room S504CD
A U.K. team has found that deep learning-powered computer-aided detection software could potentially act as a second reader for chest radiographs.
Breast MRI neural network predicts treatment response
Monday, November 27 | 11:50 a.m.-12:00 p.m. | RC215-17 | Arie Crown Theater
In this session, researchers will discuss how neural networks based on a breast MRI tumor dataset can help clinicians predict patient response to neoadjuvant chemotherapy.
AI exploits tumor imaging features to predict survival
Monday, November 27 | 11:50 a.m.-12:00 p.m. | SSC04-09 | Room E353A
Artificial intelligence (AI) can make use of tumor heterogeneity features on MRI to accurately predict the survival of metastatic colon cancer patients, according to a study by Harvard researchers.
Machine learning can help diagnose Crohn's disease
Monday, November 27 | 11:50 a.m.-12:00 p.m. | SSC05-09 | Room E451A
A machine-learning technique can diagnose Crohn's disease with high sensitivity and specificity, researchers from Italy report.
AI can detect, characterize kidney stones
Monday, November 27 | 3:40 p.m.-3:50 p.m. | SSE12-05 | Room E353A
An artificial intelligence (AI) algorithm can be used to accurately detect and characterize kidney stones, according to researchers from Boston.
Machine learning helps predict lithotripsy outcome
Monday, November 27 | 3:50 p.m.-4:00 p.m. | SSE12-06 | Room E353A
A Swiss group will present its experience in using CT texture analysis and machine learning to predict the successful treatment of kidney stones with shock-wave lithotripsy.
Deep learning helps forecast cancer treatment response
Tuesday, November 28 | 10:30 a.m.-10:40 a.m. | SSG13-01 | Room S404AB
The combination of deep learning and radiomics features could help radiologists estimate the likelihood of a bladder cancer patient responding to neoadjuvant chemotherapy.
Algorithm detects, segments lung nodules on CT
Tuesday, November 28 | 10:40 a.m.-10:50 a.m. | SSG13-02 | Room S404AB
In this scientific session, a team from imaging software developer Arterys will present its deep learning-based approach to detecting and segmenting lung nodules on CT scans.
Deep learning yields real-time coronary calcium scoring
Tuesday, November 28 | 10:50 a.m.-11:00 a.m. | SSG13-03 | Room S404AB
A deep-learning algorithm can swiftly quantify coronary artery calcium scores on low-dose CT lung screening exams, according to researchers from the Netherlands.
More may not always be better in deep learning
Tuesday, November 28 | 11:00 a.m.-11:10 a.m. | SSG13-04 | Room S404AB
Having more layers in a convolutional neural network doesn't necessarily lead to better performance for medical imaging tasks, reports a group from Chicago.
Deep learning with breast MRI boosts lesion detection
Tuesday, November 28 | 11:20 a.m.-11:30 a.m. | RC315-14 | Arie Crown Theater
A deep-learning method using multiparametric breast MRI improves automated detection and characterization of breast lesions, according to research being presented at this Tuesday morning session.
CAD enables lower radiation dose in CT lung screening
Tuesday, November 28 | 11:20 a.m.-11:30 a.m. | SSG13-06 | Room S404AB
In this talk, researchers will describe how computer-aided detection (CAD) can lead to marked reductions in radiation dose on low-dose CT lung cancer screening scans, even when image slice thickness is altered.
AI assesses cardiovascular risk on routine chest CT
Tuesday, November 28 | 11:40 a.m.-11:50 a.m. | SSG13-08 | Room S404AB
A Dutch team has found that its artificial intelligence (AI) algorithm can automatically perform cardiovascular risk assessment from routine chest CT studies.
Deep learning can spot significant coronary stenosis
Tuesday, November 28 | 11:50 a.m.-12:00 p.m. | SSG02-09 | Room S504AB
In this session, researchers will describe how deep learning shows potential for identifying patients with functionally significant coronary artery stenosis.
Machine learning differentiates brain tumors
Tuesday, November 28 | 3:10 p.m.-3:20 p.m. | SSJ19-02 | Room N228
Machine learning can distinguish between glioblastoma multiforme and primary central nervous system lymphoma on multiparametric MRI, according to researchers from Japan.
Algorithm virtually enhances resolution of microdose CT
Tuesday, November 28 | 3:10 p.m.-3:20 p.m. | SSJ22-02 | Room S403B
A major drawback of lowering CT radiation dose is a loss in image quality, but researchers found that microdose CT can deliver high-quality scans with the aid of a deep-learning algorithm.
Machine learning predicts working memory performance
Tuesday, November 28 | 3:30 p.m.-3:40 p.m. | SSJ19-04 | Room N228
This Tuesday afternoon session will reveal how machine learning can predict a person's working memory performance by analyzing brain white-matter microstructure.
Deep-learning software boosts breast US performance
Tuesday, November 28 | 3:40 p.m.-3:50 p.m. | SSJ02-05 | Room E450A
When it comes to identifying breast cancer, deep-learning software for breast ultrasound achieves diagnostic accuracy comparable to that of radiologists, Swiss researchers have found.
Finding breast cancer: How do computers compare with radiologists?
Wednesday, November 29 | 10:40 a.m.-10:50 a.m. | SSK02-02 | Room E451A
How do deep-learning algorithms compare with radiologists when it comes to finding cancer on mammography? Find out in this Wednesday morning presentation.
Machine learning can help predict KRAS mutation status
Wednesday, November 29 | 10:40 a.m.-10:50 a.m. | SSK07-02 | Room E353A
Machine learning and quantitative MRI features can assist in predicting the KRAS mutation status of tumors in patients with metastatic colon cancer, according to Harvard researchers.
CADx software performs well for bone age assessment
Wednesday, November 29 | 10:50 a.m.-11:00 a.m. | RC513-10 | Room E352
A computer-aided diagnosis (CADx) software application can accurately perform automated bone age assessment in children, a German group has found.
CAD software tracks changes in brain metastases
Wednesday, November 29 | 11:00 a.m.-11:10 a.m. | RC505-09 | Room E451B
Computer-aided detection (CAD) software can be used to detect and quantify changes in brain metastases on MRI, according to researchers from Philadelphia.
Pairing AI, radiologists improves bone age assessment
Wednesday, November 29 | 11:00 a.m.-11:10 a.m. | RC513-11 | Room E352
In this scientific session, researchers will explain why artificial intelligence (AI) and radiologists are better together when it comes to bone age assessment.
Deep learning can predict infarction risk after stroke
Wednesday, November 29 | 11:00 a.m.-11:10 a.m. | SSK15-04 | Room N226
In this morning talk, researchers will describe how deep learning can help guide treatment decisions for patients with acute ischemic stroke.
AI may enhance MRI-guided adaptive radiation therapy
Wednesday, November 29 | 3:00 p.m.-3:10 p.m. | SSM12-01 | Room S404CD
Artificial intelligence (AI) can provide automatic contouring of tumors and organs to support daily MRI-guided adaptive radiation therapy, researchers will report in this Wednesday session.
Machine learning forecasts survival in glioma patients
Wednesday, November 29 | 3:10 p.m.-3:20 p.m. | SSM12-02 | Room S404CD
Machine learning using MRI radiomic features may be able to predict the survival of patients with gliomas, according to this study from a research group in Taiwan.
Machine learning can determine onset of stroke symptoms
Wednesday, November 29 | 3:50 p.m.-4:00 p.m. | SSM12-06 | Room S404CD
A South Korean research team will describe the potential of machine learning for the crucial task of determining when acute ischemic stroke patients began experiencing symptoms.
3D CADv predicts recurrence of pulmonary nodules on CT
Thursday, November 30 | 11:40 a.m.-11:50 a.m. | SSQ18-08 | Room S403B
Researchers from Japan have demonstrated that 3D computer-aided detection and volumetry (CADv) software applied to CT scans can predict the resurgence of malignant lung nodules.
Deep learning may sharply increase specificity of CCTA
Thursday, November 30 | 11:50 a.m.-12:00 p.m. | SSQ02-09 | Room S502AB
The combination of a deep-learning algorithm and visual stenosis grading could significantly boost the specificity of coronary CT angiography (CCTA) for detecting functionally significant stenosis, a Dutch team has found.
Deep learning can quantify fat around the heart
Friday, December 1 | 10:30 a.m.-10:40 a.m. | SST02-01 | Room E450A
A deep-learning algorithm can rapidly segment and quantify the volume of thoracic fat surrounding the heart, according to researchers from Cedars-Sinai.
Deep learning can predict stenosis on fast SPECT-MPI
Friday, December 1 | 11:00 a.m.-11:10 a.m. | SST02-04 | Room E450A
Researchers have found that deep learning can improve the detection of potentially significant ischemic defects on raw, high-speed SPECT myocardial perfusion imaging (MPI) studies.
Breast MRI neural networks predict recurrence scores
Friday, December 1 | 11:20 a.m.-11:30 a.m. | SST01-06 | Room E450B
Researchers in New York have found that deep-learning networks can be trained with breast MRI data to predict Oncotype DX recurrence scores.