“The blood vessel segments are the streets and the blood flow in each segment is analogous to the traffic along each street.”
Johns Hopkins associate professor of radiology and biomedical engineering, Arvind Pathak, PhD, on a “Google Maps” approach his team developed to help visualize the blood vessel changes associated with tumor growth.
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- Medmo – Helping underinsured Americans save on medical scans by connecting them to imaging providers with unfilled schedule time.
- Nuance – AI and cloud-powered technology solutions to help radiologists stay focused, move quickly, and work smarter.
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Google’s Imaging AI
Medical imaging AI played a prominent role in this week’s Google I/O developer conference, where the search giant promoted a Google Brain algorithm trained to find early signs of cancer in CT scans that could improve survival rates by 40%. The algorithm, built through a partnership with the National Cancer Institute and Northwestern University, was specifically highlighted for spotting a patient’s early-stage cancer that was missed by 5 out of 6 radiologists.
Calling this a “a promising, but early result,” Google revealed plans for more partnerships with the medical community as it tries to advance this and other algorithms. Plans like that can certainly get the attention of the healthcare industry, especially given the growing belief that Silicon Valley is about to invade medicine, and considering that Google’s AI and Cloud businesses have consistently targeted radiology in recent years.
However, it’s worth noting that the rest of Google I/O 2019 was devoted to the company’s hardware gadgets and online widgets, and even if it pushed further into healthcare, Google is much more likely to serve as an AI technology provider than an end-to-end medical AI company.
MIT’s Breast Cancer Predictor
Researchers from MIT and MGH developed a mammography-based deep learning breast cancer risk model that’s more accurate than established clinical breast cancer models based on density and family history and performs similarly across different ethnic groups.
The team used 71,689 images to develop a trio of DL risk models (a traditional risk factor model, an image-only model, a hybrid model combining DM images and risk factors) and then compared them to the Tyrer-Cuzick breast density risk model. The hybrid DL model proved to be the most accurate (0.70 AUC, 31% of patients who later developed breast cancer ID’d as high-risk), with a statistically significant advantage over the Tyrer-Cuzick model (0.62 AUC, 18% ID’d as high-risk). The hybrid DL model was particularly accurate at predicting breast cancer among African American women, achieving the same 0.71 AUC as white women, which is notable given the Tyrer-Cuzick model’s significant racial variability (0.62 AUC white, 0.45 AUC African American).
Although more research is needed, this study provides strong evidence that mammography may be able to support risk prediction, potentially replacing conventional density-based risk models and allowing earlier breast cancer detection. Given the role of density in breast health today, that would be a really big change.
Watson Imaging Goes Live
Kentucky’s Hardin Memorial Health (HMH) became the first hospital system to go live with IBM Watson Imaging Patient Synopsis, effectively making HMH the first provider “to put Watson to use in medical imaging.” This is a pretty major milestone given the prominent roles that IBM Watson and medical imaging have respectively played in healthcare AI’s short history.
Patient Synopsis supports radiologists while they’re reading imaging studies, using radiologist-trained AI to extract patient information from EHRs and provide key insights to radiologists within a “single-view summary.” HMH, a 15-year IBM Merge Unity PACS client, will serve as a test location for Patient Synopsis, presumably before a more widespread launch.
In addition to representing a big and long-awaited achievement for IBM, this could be the starting point of a larger push into radiology by Watson Imaging (beyond IBM’s PACS/EI business). The next step may come from Watson Imaging Clinical Review (spots discrepancies between diagnostic reports and EHR problem lists), which has been scheduled to go live at Ohio’s TriHealth in 2019, while the future could bring an expansion beyond the EHR with new solutions that “leverage Watson image-analysis capabilities.” Still it’s probably wise not to look too far into the future and appreciate this announcement for what it is – the first time Watson has been used in medical imaging.
- Relatively large outpatient imaging provider, Touchstone Medical Imaging (1k employees, 60 centers in 5 states), agreed to pay $3 million and adopt a corrective action plan for a breach that exposed over 300,000 patients’ health information. In May 2014, Touchstone Medical Imaging was notified by the FBI and the HHS Office for Civil Rights (OCR) that one of its servers allowed uncontrolled online access to private patient info, “which remained visible on the Internet even after the server was taken offline.” Touchstone didn’t fully investigate the incident until several months after the notice from the FBI/OCR.
- A new paper from Brown’s Alpert Medical School shared some pretty straightforward guidelines to help make sure radiology reports can be easily read by patients. Here they are: 1. Avoid abbreviations, 2. Make report impressions as simple as possible, 3. Proofread and correct spelling and voice recognition errors, 4. Minimize technical jargon, 5. Include a summary specifically for patients, 6. Include context (e.g. stating a condition is normal for patient’s age), 7. Avoid negative language about patient healthcare decisions. The Imaging Wire fully endorses reader-friendly communication.
- Doctors in Guangzhou, China conducted an ultrasound scan on a patient from 37 miles away by using 5G wireless technology to remotely control a robotic arm holding the ultrasound scanner. The physicians highlighted 5G technology’s ability to solve previous issues regarding audio and video time lag, revealing plans to expand 5G to more medical applications in the future (e.g. consultations, surgery instructions, emergency response).
- The New York Times editorial board published a pretty fierce critique of the FDA’s approach to medical device regulation, suggesting that it adopt more rigorous testing, expand post-market surveillance, abolish the 510(k) pathway, widen its separation from device manufacturers, and strengthen guidelines around physician payments/fees. The NYT didn’t mention imaging in its editorial, but the FDA’s reaction to the rising public pressure around device regulation would almost certainly impact imaging manufacturers.
- Colombian researchers found that radiologists can just as accurately read medical images on mobile devices (smartphones and laptops) as traditional medical monitors, at least for head CT interpretations. The study looked at head CT scans from 188 patients showing symptoms of acute stroke that were read by four radiologists on the three display types, achieving very similar ROC curves (generally within 0.02 for each study type) and mean reading times for each (114 seconds monitors, 130 laptop, 143 smartphone).
- GE Healthcare and Indian tech innovation hub, NASSCOM, launched a partnership with the goal of co-developing digital health solutions for the Indian market. GE will work with Indian startups through NASSCOM’s Center of Excellence-Internet of Things (CoE-IoT) to co-creating a range of digital applications, while also working with the Indian government to help shape the country’s digital health policies. GE Healthcare is the latest of a number of major companies to partner with NASSCOM, which apparently has similar partnerships with Intel, IBM, Qualcomm, and Microsoft, among others.
- A team of Johns Hopkins Medicine researchers developed what they are calling a “Google Maps” approach to visualizing the structural and functional blood vessel changes needed for tumor growth that could lead to better predictive modeling for cancer. The model was created from 3D MR microscopy and micro-CT imaging data captured from human breast tumor cells implanted in mice. The imaging data was processed with complex mathematical formulas to create color-coded maps that represent tumors’ underlying blood vessel structure. Although not yet applicable humans, it has immediate applications for research and could eventually be used to analyze a patient’s cancer type for customized therapy.
- The Indian city of Chennai has reportedly experienced significant drops in imaging scan costs (CT and MRI scans down 30% since 2016), due to rising competition and efforts to capture unrealized demand from the city’s lower-income patients. Chennai’s radiology cost declines began after public hospitals expanded their imaging fleets/capacity and reduced their fees, prompting private centers to react with their own rate reductions and adopt some unique promotional strategies including reducing rates during evening hours or for appointments that are made using specific radiology booking apps.
- Carestream expanded its ImageView Software to its DRX-Revolution Mobile X-ray System, after initially supporting the company’s OnSight 3D Extremity Imaging System, revealing plans to make the software available across its entire portfolio. ImageView supports image processing and radiographer workflow, combining visualization processing, tube and line visualization, pneumothorax visualization, bone suppression software, pediatric image optimization / enhancement software, SmartGrid software, and access to RIS and PACS platforms.
- A team of Harvard Medical School and Brigham and Women’s Hospital researchers confirmed that there are wide variations in radiologist follow-up recommendations (up to seven-fold) and used a machine learning algorithm to reveal the factors that lead to higher follow-up recommendation rates. The researchers looked at 318,366 radiology reports (excludes breast and US imaging) from 65 radiologists, finding that 12.2% of the reports included follow-up recommendations and revealing that follow-ups were most likely to be influenced by patient age (older patients), gender (female patients), and modality (CT). However, the study found that follow-up recommendation rates were not influenced by radiologist gender, experience, or the presence of a trainee.
- Georgia became the 38th state to enact a breast density law, requiring mammography providers to inform patients about their breast density and screening options. This is a trend that will continue, especially given that the federal government passed a nationwide density notification law in February that was reinforced by the FDA’s update to the Mammography Quality Standards Act in March.
- Michigan State University (MSU) Health Care and large local radiology group, Advanced Radiology Services, formed a 50/50 joint venture. The newly-created Spartan Radiology will support the McLaren Greater Lansing hospital network as well as MSU’s radiology research and education efforts. The joint venture also represents a milestone for MSU Health Care, which replaced MSU HealthTeam in August 2018 to allow MSU to enter into service-expanding partnerships such as this.
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- This Qure.ai blog details the challenges it overcame with its qER Head CT algorithm, including developing the algorithm to support CT’s 3D images and high resolution as well as the validation hurdles it faced due to the low prevalence of abnormalities, the need to create a dataset enriched with positives, and steps required to support radiologist reading.
- Nuance’s updated PowerScribe One radiology reporting platform now provides access to AI-powered diagnostic and decision-support tools within radiologists’ familiar workflow, giving them the tools to improve efficiency and throughput, increase accuracy and specificity, and ensure evidence-based follow-up.
- Carestream’s DRX-Revolution Mobile X-ray System, DRX-Evolution Plus system, and DRX-Ascend system scored top ratings in MD Buyline’s Q1 2019 User Satisfaction Report for their performance, reliability, installation, and service.
- Focused ultrasound researchers are making unprecedented progress towards developing life-extending treatments for patients with deadly glioblastoma multiforme (GBM) brain tumors, with key blood-brain barrier (BBB)-related breakthroughs at a range of global companies, academic institutions, and research centers.
- POCUS Systems’ forthcoming ultrasounds will combine ease of use, durability, and reliability, allowing clinicians to focus on their patients.
- How much does a CT scan cost? According to Medmo, that depends. Scans made with the exact same device on the exact same body part could cost $225 at one facility and $2,500 at another. Medmo also provides some advice to make sure patients don’t pay too much for their scans, including using the Medmo Marketplace, where the average CT costs between $225 and $700.