The use of artificial intelligence (AI) in medical imaging is not new – in fact, it has been a topic of conversation at the Radiological Society of North America (RSNA) annual meeting for at least the past five years. And, with that continued growth comes the obligation among radiologists to approach its use in medical imaging in the most ethical ways possible.

On Monday, radiology ethics expert David Larson, M.D., professor radiology at Stanford University, discussed how data used in AI tools are now being applied to clinical care. That information holds tremendous value, but the industry does not yet have the ethical, regulatory, and legal constructs in place to ensure the data is handled in the most proper fashion.

To delve into the ethical issues that are facing radiology currently, as well as what radiologists can do ensure they approach this situations and opportunities appropriately, 

 spoke with Larson about what he discussed.

Videos

RSNA

What’s critical, and what radiologists can do.

AI & Medical Imaging: The Ethical Issues, Pt. 1

Radiologists who interpret scans of non-displaced fractures to the ulna in female patients can play a role in identifying a victim of intimate partner violence, a new study says.

In research presented this week during the Radiological Society of North America (RSNA) annual meeting, investigators from Brigham & Women’s Hospital shared their findings that they say supports need to screen for intimate partner violence in women who have these types of injuries.

These non-displaced fractures typically occur when someone uses their forearm to shield their face from being struck, said senior study author and radiologist Bharti Khurana, M.D. They are mostly seen in men, so when a woman presents with a break like this, it should raise a red flag.

“I would see these types of injuries in men, but one in a while, I would see them in women,” she said, discussing her own practice experience. “I never correlated it with intimate partner violence until recently.”

Radiology Spotlights Disturbing Uptick of Intimate Partner Violence During Pandemic

But, after sharing her suspicions with an orthopedic surgeon colleague, she decided to study this issue. After searching the electronic medical records from six hospitals, looking specifically for ulnar fractures in women ages 18 to 50, Khurana’s team identified 62 women who fit the description. Their average age was 31, and 12 had been confirmed as victims of intimate partner violence. Another eight were suspected.

Based on her analysis, she determined that intimate partner violence was strongly associated with X-ray that included a minimally displaced fracture.

“The radiological characteristics we were looking at were the location of the fracture, the pattern of the fracture in terms of how it broke, and the displacement of the fracture,” said lead study author David Sing, M.D., an orthopedic surgery resident at Boston Medical Center. “Out of all those things, what we usually saw was a minimally displaced fracture, meaning the bone is broken all the way through but has not shifted significantly.”

In many cases, Khurana explained, women who have ulnar fractures due to intimate partner violence typically blame a fall. However, falls are much more likely to result in a break in the arm’s other bone, the radius.

“It’s actually rare to break your ulna in a fall,” Khurana said. “If a radiology is seeing an ulnar fracture that is non-displaced, and the woman says she had a fall, it’s actually quite concerning for intimate partner violence.”

This finding was bolstered by the fact that four out of eight intimate partner violence victims reported their injuries as the outcome of a fall, but the ones who were not sustained the ulnar fractures from car accidents or an accidental strike, such as hitting a tree while skiing.

The frequency of this type of injury among victims of intimate partner violence underscores the need for greater screening, she said. In only 40 percent of confirmed or suspected cases did providers do a formal evaluation or screening. Consequently, in many cases, the radiologist who sees such a fracture on an image can help improve these efforts by alerting the emergency department physician or orthopedic surgeon that an ulnar fractur frequently accompanies intimate partner violence, giving the ordering physician the opportunity to dig deeper into the patient’s history. In fact, she said historical imaging analysis by itself raised the red flag with 75 percent of confirmed intimate partner violence patients.

“Careful analysis of previous imaging exams may also help radiologists confirm their suspicion of intimate partner violence,” said study co-author Rahul Gujrathi, M.D., a Brigham & Women’s radiology fellow.

The ability to recognize the signs of intimate partner violence is particularly critical now since the rate of 

 has increased significantly during the pandemic. A quick diagnosis can be substantially beneficial for the patient.

“The sooner we can address and change the behavior, the better,” she said. “Just like radiologists want to diagnose cancer as early as possible, it’s the same thing with this. If we diagnose early, we have a better chance to break the cycle of violence.”

Articles

Radiologists have the opportunity to identify women suffering intimate partner violence by detecting a specific type of forearm fracture. 

With Forearm Fracture in Women, Suspect Domestic Violence

A photo of a chest X-ray captured by a smartphone can be enough for providers to diagnose tuberculosis (TB), according to new research.

In a poster presented during this year’s Radiological Society of North America (RSNA) annual meeting, researchers from National Tsing Hua University in Taiwan explained how using a deep-learning TB detection model with those smartphone pictures can impact diagnosis of the disease. This is the first study to pair a deep learning model with a smartphone for this purpose.

This method is critical, said lead study author Po-Chih Kuo, Ph.D., assistant professor of computer science, because early TB diagnosis has historically been difficult in resource-poor countries where radiologists and high-resolution images are not always available. By deploying the algorithm – called TBShoNet – in a smartphone, providers will be better equipped to make a diagnosis on their own.

“We need to extend the opportunities around medical artificial intelligence to resource-limited settings,” Kuo said.

Kuo’s team used a database of 250,044 chest X-rays with 14 pulmonary labels that did not include TB to pre-train the neural network. They, then, recalibrated the model for chest X-ray photographs, using simulation methods to augment the dataset. Adding an additional two-layer neural network that was trained on augmented chest X-ray images to the pre-trained model completed TBShoNet.

To test TBShoNet, Kuo’s team took 662 chest X-ray photographs – 336 TB and 326 normal – with five different smartphones. According to their analysis, the model produced 81 percent sensitivity and 84 percent specificity for TB classification.

Applying a deep-learning model to a photograph of a chest X-ray can help providers in resource-poor areas diagnose the disease.

Using AI Can Help Diagnose Tuberculosis on a Smartphone

A deep learning algorithm that pulls biomarkers from screening mammograms can outperform traditional risk assessment models when predicting a patient’s individual breast cancer risk.

A team from Massachusetts General Hospital, led by breast radiologist Leslie Lamb, M.D., M.Sc, will present their findings during the Radiological Society of North America (RSNA) annual meeting this week.

“Traditional risk assessment models do not leverage the level of detail that is contained within a mammogram,” Lamb said. “Even the best existing traditional risk models may separate sub-groups of patients but are not as precise on the individual level.”

In fact, she said, current traditional risk assessment models rely on a sliver of patient data, including family history, prior breast biopsies, and hormonal and reproductive history. Breast density is the only bit of information pulled from screening mammograms that is incorporated into these models.

Expanded Risk Model Identifies Women Who Need Additional Breast Cancer Screening

Making use of only that one piece of information undermines the potential ability to give women individualized assessments of their personal risk, the team asserted.

“Why should we limit ourselves to only breast density when there is such rich digital data embedded in every woman’s mammogram,” said Constance D. Lehman, M.D., Ph.D., senior study author and division chief of breast imaging at MGH. “Every woman’s mammogram is unique to her just like her thumbprint. It contains imaging biomarkers that are highly predictive of future cancer risk, but until we had the tools of deep learning, we were not able to extract this information to improve patient care.”

To develop this deep learning algorithm, which has been externally validated in Sweden and Taiwan, Lamb’s team gathered data from women with a personal history of breast cancer, implants, or prior biopsies who were seen at one of five MGH breast screening locations. Overall, they included 245,753 consecutive 2D digital bilateral screening mammograms performed in 81,818 patients between 2009 and 2016. Of these exams, 210,819 studies from 56,831 patients were used for training, 25,644 from 7,021 women were used for testing, and 9,290 exams from 3,961 patients were used for validation.

AI Risk Score Predicts Breast Cancer Risk Better Than Mammographic Density

The team, then, used statistical analysis to figure out how well the deep learning model performed in predicting future breast cancer within five years of the index mammogram when compared to an existing commercially available risk assessment model, particularly the Tyrer-Cuzick version 8. The outperformance was significant, the team said – the deep learning model had a predictive rate of 0.71, but the traditional model only reached a 0.61 rate.

“Our deep learning model is able to translate the full diversity of subtle imaging biomarkers in the mammogram that can predict a woman’s future risk for breast cancer,” Lamb said, noting that future studies are planned with larger African American and minority populations.

Ultimately, she said, using a deep learning model can save both time and money.

“Traditional risk models can be time-consuming to acquire and rely on inconsistent or missing data,” she said. “A deep learning image-only risk model can provide increased access to more accurate, less costly risk assessment and help deliver on the promise of precision medicine.”

For more coverage based on industry expert insights and research, subscribe to the Diagnostic Imaging e-Newsletter 

A model that uses biomarkers pulled from a woman’s mammogram does produce a more accurate breast cancer risk assessment.

Deep Learning Model Outperforms Traditional Breast Cancer Risk Assessment

Cooking with wood or other biomass fuels can put people at greater risk for significant lung damage, and CT scans can reveal the impact pollutants and biotoxins make.

Results of a multidisciplinary study into the effect of cookstove pollutants, led by Eric A. Hoffman, Ph.D., University of Iowa radiology medicine, and biomedical engineering professor, will be presented during next week’s Radiological Society of North America (RSNA) annual meeting.

Although public health initiatives actively promote the use of cleaner-burning fuels, such as liquified petroleum gas, approximately 3 billion people worldwide still rely on biofuels, such as wood or dried brush, to cook. And, the pollutants produced by this type of cooking contribute to household air pollution-related illness, killing approximately 4 million people annually.

Being able to assess the effects on the lungs is important, said study co-author Abhilash Kizhakke Puliyakote, Ph.D., a postdoctoral researcher from the University of California San Diego School of Medicine. But, existing tests do not do an adequate job in evaluation.

“The extent of damage from biomass fuels is not really well captured by traditional tests,” he said. “You need more advanced, sensitive techniques like CT imaging. The key advantage to using imaging is that it’s so sensitive that you can detect subtle, regional changes before they progress to full blown disease, and you can follow disease progression over short periods of time.”

For their study, the team, which also included researchers from Periyar Maniammai Institute of Science and Technology, analyzed the impact of cookstove pollutants in 23 people who cooked with liquified petroleum gas or wood biomass in Thanjavur, India. After measuring the concentration of pollutants in their homes, the team tested their lung function using spirometry.

In addition, the team captured CT scans both when a participant inhaled and when he or she exhaled, measuring the difference between the images to determine lung function. Their analysis revealed that participants who cooked with wood biomass had greater concentrated exposures to pollutants and bacterial endotoxins than those who used petroleum liquified gas.

Wood biomass-burning study enrollees also faced an additional problem – they had significantly higher levels of air trapping in their lungs. This condition, associated with lung disease and often seen in tobacco smokers, occurs when part of the lungs cannot take in enough oxygen or expel carbon dioxide. In roughly one-third of the biomass users, 50 percent of the air they inhaled was ultimately trapped in their lungs, the team said.

According to Puliyakote, the lack of emphysema seen in this group points to a greater impact on the small airways of the lungs. Even without any obvious symptoms or breathing problems, he said, lungs can still experience injury and inflammation. Consequently, it is possible that many people can have undetected and unaddressed problems.

“For people exposed to biomass smoke for any extended duration, it is critical to have a complete assessment of lung function by healthcare professionals to ensure that any potential injury can be resolved with appropriate interventions,” he said.

In addition to offering insights into how biomass smoke from cooking acts on the lungs, this study’s findings can also add knowledge around the impact of the wildfires across the United States, as well.

CT scans reveal impaired lung function in individuals who use biomass, such as wood or wildfires, to cook.

Cooking with Wood? Smoke Pollutants Could Make It Harder to Breathe

Anxiety can do more than augment daily stress levels. New research with brain MRIs shows it is also associated with a faster transition from mild cognitive impairment to Alzheimer’s disease.

Deaths associated with Alzheimer’s disease have more than doubled since 2000, and many of these individuals start down the path by suffering mild symptoms, such as declines in memory and thinking skills. Many also exhibit signs of anxiety, but little is known about the role anxiety might actually play in cognitive decline.

In research that will be presented next week during the Radiological Society of North America (RSNA) annual meeting, investigators from the Medical University of South Carolina (MUSC) explored that question.

“We know that volume loss in certain areas of the brain is a factor that predicts progression to Alzheimer’s disease,” said senior study author Maria Vittoria Spampinato, M.D., MUSC radiology professor. “In this study, we wanted to see if anxiety had an effect on brain structure, or if the effect of anxiety was independent from brain structure in favoring the progression of disease.”

To get a better understanding of the impact of anxiety, Spampinato’s team examined brain MRI scans of 339 patients included in the Alzheimer’s Disease Neuroimaging Initiative 2 cohort. The average age was 72, and each patient had a baseline diagnosis of mild cognitive impairment. Of the group, 72 progressed to Alzheimer’s and the remaining 267 stayed cognitively stable.

Using these scans, the team identified baseline hippocampus and entorhinal cortex volumes. These areas are integral to forming memories, they said. In addition, they tested study participants for the presence of the ApoE4 allele, the most prevalent genetic risk factor for Alzheimer’s disease. For anxiety measurements and assessments, they conducted clinical surveys.

Their evaluation confirmed some expectations. Patients who progressed through to Alzheimer’s did, indeed, have significantly lower hippocampus and entorhinal cortex volumes, as well as a greater incidence of the ApoE4 allele.

The team also made an additional discovery – anxiety was independently associated with cognitive decline. Those study participants with anxiety symptoms did progress to Alzheimer’s faster regardless of whether they had the ApoE4 allele or had brain volume loss. Being able to identify this link, they said, opens the door to improving how patients with mild cognitive impairment are screened and managed.

“We need to better understand the association between anxiety disorders and cognitive decline,” Spampinato said. “We don’t know yet if the anxiety is a symptom – in other words, their memory is getting worse and they become anxious – or if anxiety contributes to cognitive decline. If we were able in the future to find that anxiety is actually causing progression, then we should more aggressively screen for anxiety disorders in the elderly.”

For example, said Jenny L. Ulber, MUSC medical student and first author, it could be helpful to conduct an assessment for anxiety disorders alongside the depression evaluations completed on elderly patients who are in the hospital.

“Middle-aged and elderly individuals with high levels of anxiety may benefit from intervention,” she said, “whether it be pharmacological or cognitive behavior therapy, with the goal of slowing cognitive decline.”

Their study did have a limitation of relying on MRI scans conducted at one moment in time. For future work – and to continue to better understand this link between anxiety and brain structure – the team plans to study MRIs collected after initial scans.

“We’re now interested in looking at changes over time to see if anxiety has an effect one way or the other on how fast the brain damage progresses,” Spampinato said. “We will also take a closer look at gender differences in the association between anxiety and cognitive decline.”

Brain MRI scans reveal that anxiety is independently associated with a faster progression from mild cognitive impairment to Alzheimer’s disease.

Anxiety Goes Beyond Stress – It Can Speed Up Alzheimer’s

A common weight-loss surgery in adolescents – sleeve gastrectomy – might induce weight loss, but, according to CT and MRI scans, it also weakens the patient’s bones.

In a sleeve gastrectomy procedure, 75 percent of the patient’s stomach is removed to control food intake, promoting weight loss. As a result, the stomach becomes tube- or sleeve-shaped, and from 2005 to 2014, the number of these procedures performed on adolescents has increased 100-fold, said a team from Harvard Medical School.

“Childhood obesity is a major public health issue that has increased over the last 10 years,” said lead investigator Miriam A. Bredella, M.D., radiology professor at Harvard and vice chair of the Massachusetts General Hospital radiology department. “Sleeve gastrectomy is the most common bariatric surgery procedure performed in children and adults.”

Bredella’s team will present their findings next week during the Radiological Society of North America (RSNA) annual meeting.

It is known that bariatric surgery has long-term bone effects in adults that can lead to a higher risk of fracture. The team set out to determine if the same thing happens to teen-agers who have the surgery during their critical years of growth and development when they are accruing bone mass.

For their study, the team enrolled 52 adolescents who had moderate-to-severe obesity with an average body mass index (BMI) of 45. A BMI over 30 is considered obese. The average age was 17.5 years, and 38 of the participants were girls. Bredella’s team split the group in half – 26 participants underwent sleeve gastrectomy, and 26 did not.

To detect any changes, all participants had a quantitative CT to detect volumetric bone mineral density changes in the lumbar spine performed both before any surgery and 12 months later. The test is particularly accurate for this measurement, the team said. In addition, the team also performed proton MR spectroscopy on all participants to quantify the amount of bone marrow fat in the lumbar spine. Recent studies have shown that bone marrow fat can be a bone quality biomarker because it responds to nutritional changes.

Based on the results of these scans, the team determined that adolescents who had the surgery lost 34 kilograms/75 pounds, and the participants who did not have the procedure saw no significant weight loss. But, the teens who had the surgery also saw a significant increase in bone marrow fat with a corresponding decrease in bone density in the lumbar spine.

“Adolescents who underwent sleeve gastrectomy had bone loss and an increase in bone marrow fat, despite marked loss of body fat,” Bredella said. “While weight-loss surgery is successful for weight loss and improving metabolic disorders, it has negative effects on bone.”

This post-surgery change was not surprising, though, she said, because greater bone density comes with greater weight-bearing. And, alongside, those drops in bone density, the surgery also disrupted hormones and nutrients that are integral to bone health.

Knowing how this surgery affects young patients can be important to potentially safeguarding their bone quality as they age, Bredella said.

“We need to identify mechanisms that will help prevent bone loss in these patients and to make adolescents with obesity more aware of bone health,” she said. “Adolescence is the critical time for bone mass accrual, and any process that interferes with bone accrual during this time can have dire consequences later in life.”

Images reveal that adolescents experienced a loss in volumetric bone mineral density after sleeve gastrectomy and extreme weight loss.

CT, MRI Show Weaker Bones in Teens Post-Weight Loss Surgery

One of the biggest challenges during the COVID-19 pandemic has been whether facilities have enough supplies to conduct the nasopharyngeal (NP) tests for diagnosis. The University of South Florida (USF) radiology department has a solution to this problem – 3D printed NP swabs.

USF researchers, led by Summer Decker, Ph.D., associate professor, vice chair for research, and director of the radiology department’s 3D Clinical Applications, immediately recognized at the beginning of the pandemic that there would be a need for more swabs. Using their 3D printer, they built the swabs and ran a clinical trial.

Their swabs have already received national and international recognition for applying 3D medical printing power in a novel way to create an innovative clinical solution. The results of this first clinical trial will be presented during the Radiological Society of North America (RSNA) annual meeting next week.

“To date, USF Health has printed more than 100,000 3D NP swabs, and hospitals around the world have used our 3D files to print tens of millions more swabs for point-of-care use,” Decker explained. 

Largely, providers worldwide are using a flocked NP swab that contains a narrow plastic rod and a tip covered in a flocked polyester material to collect nasal secretion samples from the back of the nose and throat. It is considered the current standard of care for lab analysis and COVID-19 diagnosis. As the pandemic has raged on, however, supplies have become scarce in many areas.

To address the need, Decker’s team created interdisciplinary partnerships that were born out of the RSNA 3D Printing Special Interest group. The project, she said, highlights the unique ways radiology departments can impact hospitals and clinical care during global crises.

“We collaborated with our colleagues in infectious disease, virology, emergency medicine, and radiology, as well as Todd Goldstein, Ph.D., at Northwell Health System’s 3D Design and Innovation Lab in New York City. The city was the U.S. epicenter of virus infection at the time,” she said. “In a matter of days, we came up with 12 designs and printed three to test on ourselves.”

Using FormLabs printers and surgical grade resin, Decker’s team developed their final prototype and submitted it to infectious disease specialists at both USF and Northwell for validation testing, ensuring the swab could gather enough viral cells and hold them for up to three days. They also needed to ensure the resin would not interfere with test results.

For the clinical trial, the USF team tested the swab at numerous clinical sites, including Tampa General Hospital, Northwell Hospital, and Thomas Jefferson University Hospital in Philadelphia. At those three sites, they tested 291 patients, between ages 14 to 94, who were either hospitalized or seen in the emergency department with both the flocked and 3D-printed swabs.

The results, Decker said, were statistically identical in a head-to-head trial.

“The results were overwhelmingly positive,” she said. “The clinical trial showed that the 3D nasal swabs performed as well as – or, in some cases, better than – flocked swabs.”

As a result, Tampa General Hospital has changed its standard of care, adopting the 3D swabs and printing more than 300 swabs a day for both the hospital and its affiliated care centers. All total, the hospital’s six printers can produce 9,000 swabs each week.

The printing process lasts roughly 15 hours, during which the printed swabs are rinsed in isopropyl alcohol, cured, and hand inspected for defects. Infectious disease experts also examine the swabs before they are sterilized in an autoclave and packed into a test kit.

While USF has filed a provisional patent on the 3D NP swab, it has made the design files and clinical data available to hospitals, clinics, and licensed medical device companies around the world at no cost.

“We wanted to get this swab in as many hands as possible to help slow the spread of the virus,” Decker said. “This is the resultl of many people working together to make one device to help others.”

Swabs created with a 3D printer produce virtually identical diagnostic results, effectively addressing the shortage of nasopharyngeal swabs.

3D Printed Nasal Swabs as Effective as Flocked Swabs in Diagnosing COVID-19

Detecting Parkinson’s disease earlier could be as simple as applying an artificial intelligence algorithm to a regular photograph, potentially replacing the use of CT or MRI brain images.

In research that will be presented during the Radiological Society of North America (RSNA) annual meeting next week, investigators from the University of Florida in Gainesville have deployed a type of artificial intelligence (AI) called support vector machine (SVM) learning to pick up on early signs of Parkinson’s disease that can be captured in normal pictures of the back of the eye.

This tactic can be used to replace the traditional method of diagnosing Parkinson’s disease – simply waiting until the patient begins to exhibit the characteristic tremors, muscle stiffness, and impaired balance that accompany this progressive central nervous system disorder.

“The issue with that method is that patients usually develop symptoms only after prolonged progression with significant injury to dopamine brain neurons,” said lead study author Maximillian Diaz, a biomedical engineering doctoral student at the University of Florida. “This means that we are diagnosing patients late in the disease process.”

Instead, he said, an eye exam can provide clues that can lead to early diagnosis and therapy. As Parkinson’s progresses, nerve cell decay wears away the retinal walls, and it also impacts the retina’s microvasculature. With SVM, providers can pick up on these changes, supporting the theory that evidence of brain physiology alterations can be found in the eye.

Currently, to find problems in the brain, providers must use costly MRI, CT, and nuclear medicine techniques to capture brain images. Instead, SVM can be used with the basic photography equipment that most eye clinics have available in-office. In fact, Diaz said, this new technique also works with a smartphone that is equipped with a special lens.

“It’s just a simple picture of the eye, you can have it done in less than a minute, and the cost of the equipment is much less than a CT or MRI machine,” Diaz said. “If we can make this a yearly screening, then the hope is that we can catch more cases sooner, which can help us better understand the disease and find a cure or a way to slow the progression.”

In addition to detecting Parkinson’s, he said, it is possible this approach could also be used with the identification of Alzheimer’s and multiple sclerosis.

For this study, Diaz worked with graduate student Jianqiao Tian and University of Florida neurologist Adolfo Ramirez-Zamora, M.D. The team was led by Ruogu Fang, Ph.D., director of the J. Crayton Pruitt Department of Biomedical Engineering's Smart Medical Informatics Learning and Evaluation Lab (SMILE).

Basic photographs paired with AI technique can pick up on retinal changes that are early signs of the progressive central nervous system disorder.



Eye Exam Plus AI Could Replace Advanced Imaging in Parkinson’s Detection

COVID-19-positive patients who have hypertension and type 2 diabetes are more likely to experience the neurological complications, including bleeding in the brain and stroke, that come with the virus.

Based on CT and MRI images, investigators from the Perelman School of Medicine at the University of Pennsylvania determined that these chronic conditions could play a role in which patients are impacted by more than just the hallmark lung inflammation that comes with viral infection.

“COVID-19’s effects extend far beyond the chest,” said lead study author Colbey W. Freeman, M.D., chief resident in the Penn Medicine radiology department. “While complications in the brain are rare, they are an increasingly reported and potentially devastating consequence of COVID-19 infection.”

Acute Stroke Predicts Negative Outcome in COVID-19 Patients with Neuroimaging Findings

Freeman’s team, including Jonathan Masur, M.D., Mougnyan Cox, M.D., and Suyash Mohan, M.D. will present their findings during this year’s Radiological Society of North America annual meeting. 

In an effort to better understand how COVID-19 impacts the body, Freeman’s team examined head CT and/or MRI images for patients positive for the virus who presented to the University of Pennsylvania health system from January 2020 to April 2020. All total, 81 of the 1,357 COVID-19-positive patients who were admitted underwent a brain scan prompted by either altered mental state or focal neurological deficits, including speech and vision problems.

Based on the team’s analysis, 18 patients – slightly over 20 percent – had findings that were considered critical, such as strokes, brain bleeds, or blocked blood vessels. And, of that group, at least half also had a pre-existing history of high blood pressure or type 2 diabetes.

What COVID-19 Brain Abnormalities Could Mean

“COVID-19 is associated with neurological manifestations, and hypertension and type 2 diabetes mellitus are common in individuals who develop these manifestations,” Freeman said. “These populations may be at higher risk for neurologic complications and should be monitored closely.”

The team also found that two-thirds of the patients who had critical findings were African American, indicating COVID-19 positive patients in that minority group should be monitored more closely. And, among the entire group of 18 with emergent findings, three died while in the hospital, the team said.

Although this study revealed a role for high blood pressure and type 2 diabetes in the neurological impact of the virus, investigators still do not know exactly what causes those complications. The mechanisms could be multi-factorial, and the generally accepted belief is that infection-associated inflammation is responsible, they said. In this study, in particular, they reported, blood markers for inflammation were high in patients who had critical results.

“When your body is in an inflammatory state, it produces all these molecules called cytokines to help recruit the immune system to perform its function,” Freeman said. “Unfortunately, if cytokines are over-produced, the immune response actually starts doing damage.”

Using data from this study, the team is also looking at the how frequently COVID-19-positive patients on extracorporeal membrane oxygenation (ECMO) – a pump system that circulates and replenishes oxygen in the blood – experience neurological complications. Many patients included in this study required ECMO during hospitalization. 

Future plans include a larger prospective study to investigate delayed, long-term, and chronic neurologic manifestations that have not currently been identified, but that might manifest later.

Approximately half of all patients who experience stroke, brain bleeds, or blocked blood vessels, identified on MRI or head CT in this study, had high blood pressure or type 2 diabetes.

Latest Conference Articles