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Diabetic retinopathy -- a disorder characterized by damage to the small blood vessels lining the retina (light-focusing area) of the eye and a leading cause of vision loss worldwide -- has been on the rise in recent years as the number of children and adolescents diagnosed with either type 1 or type 2 diabetes increases. Although the American Diabetes Association (ADA) advises regular screening for pediatric diabetic retinopathy, it's estimated that fewer than half of all youth with diabetes follow the recommendation. Without early detection and treatment, these patients put themselves at risk for serious vision problems or blindness as they get older.
In a recent study reported online Jan. 21, 2021, in Diabetes Care, researchers in pediatric endocrinology and ophthalmology at Johns Hopkins Medicine and three other U.S. medical institutions demonstrated that autonomous artificial intelligence (AI) can be used to detect pediatric diabetic retinopathy with high sensitivity, specificity and diagnosability (accuracy of detection) -- and without the need for human interpretation. The technique had already been approved for adults with diabetes by the U.S. Food and Drug Administration and is part of the ADA's retinopathy screening guidelines for patients age 21 or older.
Because the AI screening does not require eye dilation, it takes less time to perform and is easier for pediatric patients to undergo. Therefore, the adherence of the patients in this study to getting regular retinopathy screenings, as defined by the ADA, more than doubled.
"Use of autonomous AI in adults has shown extremely high levels of sensitivity, specificity and accuracy in diagnosing referable [more than mild] diabetic retinopathy, when the disease is most treatable," says Risa Wolf, M.D., study lead author, Johns Hopkins Children's Center pediatric endocrinologist and assistant professor of pediatrics at the Johns Hopkins University School of Medicine. "So, with the rising incidence of pediatric diabetes -- especially type 2, which is associated with an earlier onset of retinopathy -- we felt it was important to see if AI could make an improvement in adherence to screening guidelines and early diagnosis for younger patients."
A total of 310 pediatric patients with diabetes were recruited over a 12-month period for the study. The participants had a mean age of 12, were 47% male and represented a broad range of ethnicities (57% white, 32% Black, 4% Hispanic, and 7% Asian or other). Patients predominantly had type 1 diabetes (82%) and a mean age of 9 at first diagnosis of diabetes, whether type 1 or type 2.
One hundred fifty-two participants (49%) reported having a diabetic eye exam with dilation before joining the study, but only 17 (11.3%) had a record of the screening test in their case files. However, using a special statistical calculation, the researchers were able to measure the improvement in screening adherence for these patients and then estimate it for the entire group.
In the study, digital fundus photography -- which does not require dilation, takes only a few minutes and produces high-quality images for detection of retinopathy by trained observers -- was used in conjunction with a fully autonomous AI system built into the camera. This eliminated the need for human evaluation to get a diagnosis.
For verification of the diagnoses made by the AI system, the same color photographs were reviewed independently by two retina specialists who were not told of the AI interpretations.
Of the 310 participants, AI gave an accurate interpretation for retinopathy or no retinopathy in 302 (97.5%) cases. The eight image sets not interpreted were due to the participant's inability to keep his or her eyes open during the photographic flash or to focus as needed.
Overall, sensitivity (85.7%), specificity (79.3%) and diagnosability (97%) of the AI interpretations in children were high, based on the reference standards for these characteristics defined by retina specialists. This high level was seen regardless of race, ethnicity, age and sex.
After implementing the AI screening system, the adherence rate improved from 49% to 95%, an increase of 111%.
"Our results show that autonomous AI -- proven as a safe and effective means of diagnosing diabetic retinopathy in adults -- also deserves a role in screening for this disease in younger patients," says retina specialist Roomasa Channa, M.D., senior study author and assistant professor of ophthalmology and visual sciences at the University of Wisconsin School of Medicine and Public Health.
Wolf is available for interviews.
The longer someone stays awake, the more likely they'll start getting tired as their brain needs sleep. But how the brain senses that need for sleep hasn't always been clear. Now, Johns Hopkins Medicine researchers have shown in fruit flies that certain groups of brain cells called astrocytes sense electrical activity in different regions of the brain and use these signals to facilitate the process of falling asleep. The more activity that they detect, the stronger the need-for-sleep signals become, until they trigger a release mechanism that pushes sleep.
In their findings, published Jan. 11, 2021, in the journal Current Biology, the researchers say that understanding how we get sleepy may help us understand and eventually treat the kinds of sleep disorders in people who never feel rested no matter how much sleep they get.
"When you nod off in class during a boring lecture but still hear the professor calling your name, that is because only part of your brain is asleep," says Mark Wu, M.D., Ph.D., professor of neurology at the Johns Hopkins University School of Medicine. "We believe that different groups of these astrocyte cells monitor different parts of the brain to initiate sleep drive in those specific regions."
The researchers demonstrated in their study that prolonged wakefulness results in a buildup of calcium ions in the astrocytes, which eventually triggers a whole cascade of genes to be turned on. When this happens, the astrocytes release chemical molecules that induce sleep by acting on a central sleep drive circuit (an electrochemical network) in the brain.
Two recent publications by researchers at other institutions showed similar findings in mice to the results published in the Johns Hopkins Medicine paper. Together, these studies suggest that these processes are conserved across the animal kingdom and are likely applicable to humans as well.
Wu is available for interviews.
Johns Hopkins Medicine researchers recently found that although primary care physicians should discuss the problems of hypoglycemia, or low blood sugar, during each visit with patients who have diabetes and take high-risk medications such as insulin, the topic was only talked about in a quarter of those visits.
Hypoglycemia is the most common serious side effect caused by diabetes treatment. Severe hypoglycemic episodes can lead to negative consequences, including falls and emergency department visits, and may increase the risk for stroke and death. In a 2018 survey of 20,188 adults with diabetes, 12% reported experiencing severe hypoglycemia within the previous year.
"For patients to have safe diabetes treatment, there needs to be open communication between them and their healthcare provider about medication side effects, especially hypoglycemia," says Scott Pilla, M.D., M.H.S., assistant professor of medicine at the Johns Hopkins University School of Medicine. "For example, we found in our study that clinicians almost never counseled against driving a car if a patient thinks his or her blood sugar is low or may become low. This is an important discussion to have because low blood sugar could cause a person to think unclearly and have an accident."
Most outpatient diabetes treatment in the United States occurs in primary care offices, so doctor visits with patients who have diabetes offer a critical opportunity to promote hypoglycemia prevention. To find ways of improving hypoglycemia communication during doctor visits, Pilla and his team sought to define the frequency and content of assessments and counseling provided in the primary care setting related to hypoglycemia.
To do this, the researchers examined 83 primary care visits from one urban health practice, representing eight clinicians seeing 33 patients with diabetes who used insulin or sulfonylureas such as glipizide and glyburide. Audio during the visits was recorded as part of the Achieving Blood Pressure Control Together study, a randomized trial of behavioral interventions for high blood pressure.
Communication between the clinician and patient about hypoglycemia occurred in 24% of visits, while communication about hypoglycemia prevention took place in 21%. Despite patients voicing fear of hypoglycemia, clinicians rarely assessed hypoglycemia frequency, its severity or the impact it may have on the patient's quality of life.
While office visits are sometimes complicated and often focus on a variety of topics, Pilla says the study findings should encourage primary care clinicians to make hypoglycemia assessment counseling a priority for patients taking high-risk diabetes medications. He says that a system to routinely assess for hypoglycemia in primary care visits is currently lacking and he believes that his team's research shows the need for one.
Pilla also suggests that patients speak up about low blood sugar during medical visits. "Primary care clinicians should work together with patients to figure out how to best prevent low blood sugar episodes and choose the safest diabetes treatment," he says.
Pilla says he ultimately hopes to examine communications about hypoglycemia on a larger scale. With more data, he explains, researchers can better understand how to make such discussions more effective and productive, which could lead to improved safety for diabetes treatment in primary care.
Pilla is available for interviews.