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When a patient signs on to a telehealth portal, there’s little more a provider can do than ask questions. But a new artificial intelligence (AI) technology could allow providers to get feedback about the patient’s blood pressure and diabetes risk just from a video call or a smartphone app.
Researchers at the University of Tokyo in Japan are using AI to determine whether people might have high blood pressure or diabetes based on video data collected with a special sensor.
The technology relies on photoplethysmography (PPG), which measures changes in blood volume by detecting the amount of light absorbed by blood just below the skin.
Wearable devices like Apple Watches and Fitbits also use PPG technologies to detect heart rate and atrial fibrillation.
“If we could detect and accurately measure your blood pressure, heart rate, and oxygen saturation non-invasively that would be fantastic,” said Eugene Yang, MD, professor of medicine in the division of cardiology at the University of Washington School of Medicine in Seattle who was not involved in the study.
How Does PPG Work — and Is This New Tech Accurate?
Using PPG, “you’re detecting these small, little blood vessels that sit underneath the surface of your skin,” explained Yang.
“Since both hypertension and diabetes are diseases that damage blood vessels, we thought these diseases might affect blood flow and pulse wave transit times,” said Ryoko Uchida, a project researcher in the cardiology department at the University of Tokyo and one of the leaders of the study.
PPG devices primarily use green light to detect blood flow, as hemoglobin, the oxygen-carrying molecule in blood, absorbs green light most effectively, Yang said. “So, if you extract and remove all the other channels of light and only focus on the green channel, then that’s when you’ll be able to potentially see blood flow and pulsatile blood flow activity,” he noted.
The University of Tokyo researchers used remote or contactless PPG, which requires a short video recording of someone’s face and palms, as the person holds as still as possible. A special sensor collects the video and detects only certain wavelengths of light. Then the researchers developed an AI algorithm to extract data from participants’ skin, such as changes in pulse transit time — the time it takes for the pulse to travel from the palm to the face.
To correlate the video algorithm to blood pressure and diabetes risk, the researchers measured blood participants’ pressure with a continuous sphygmomanometer (an automatic blood pressure cuff) at the same time as they collected the video. They also did a blood A1c test to detect diabetes.
So far, they’ve tested their video algorithm on 215 people. The algorithm applied to a 30-second video was 86% accurate in detecting if blood pressure was above normal, and a 5-second video was 81% accurate in detecting higher blood pressure.
Compared with using hemoglobin A1c blood test results to screen for diabetes, the video algorithm was 75% accurate in identifying people who had subtle blood changes that correlated to diabetes.
“Most of this focus has been on wearable devices, patches, rings, wrist devices,” Yang said, “the facial video stuff is great because you can imagine that there are other ways of applying it.”
Yang, who is also doing research on facial video processing, pointed out it could be helpful not only in telehealth visits, but also for patients in the hospital with highly contagious diseases who need to be in isolation, or just for people using their smartphones.
“People are tied to their smartphones, so you could imagine that that would be great as a way for people to have awareness about their blood pressure or their diabetes status,” Yang noted.
More Work to Do
The study has a few caveats. The special sensor they used in this study isn’t yet integrated into smartphone cameras or other common video recording devices. But Uchida is hopeful that it could be mass-produced and inexpensive to someday add.
Also, the study was done in a Japanese population, and lighter skin may be easier to capture changes in blood flow, Uchida noted. Pulse oximeters, which use the same technology, tend to overestimate blood oxygen in people with darker skin tones.
“It is necessary to test whether the same results are obtained in a variety of subjects other than Japanese and Asians,” Uchida said, in addition to validating the tool with more participants.
The study has also not yet undergone peer review.
And Yang pointed out that this new AI technology provides more of a screening tool to predict who is at high risk for high blood pressure or diabetes, rather than precise measurements for either disease.
There are already some devices that claim to measure blood pressure using PPG technology, like blood pressure monitoring watches. But Yang warns that these kinds of devices aren’t validated, meaning we don’t really know how well they work.
One difficulty in getting any kind of PPG blood pressure monitoring device to market is that the organizations involved in setting medical device standards (like the International Organization for Standards) doesn’t yet have a validation standard for this technology, Yang said, so there’s really no way to consistently verify the technology’s accuracy.
“I am optimistic that we are capable of figuring out how to validate these things. I just think we have so many things we have to iron out before that happens,” Yang explained, noting that it will be at least 3 years before a remote blood monitoring system is widely available.
A version of this article first appeared on Medscape.com.
When a patient signs on to a telehealth portal, there’s little more a provider can do than ask questions. But a new artificial intelligence (AI) technology could allow providers to get feedback about the patient’s blood pressure and diabetes risk just from a video call or a smartphone app.
Researchers at the University of Tokyo in Japan are using AI to determine whether people might have high blood pressure or diabetes based on video data collected with a special sensor.
The technology relies on photoplethysmography (PPG), which measures changes in blood volume by detecting the amount of light absorbed by blood just below the skin.
Wearable devices like Apple Watches and Fitbits also use PPG technologies to detect heart rate and atrial fibrillation.
“If we could detect and accurately measure your blood pressure, heart rate, and oxygen saturation non-invasively that would be fantastic,” said Eugene Yang, MD, professor of medicine in the division of cardiology at the University of Washington School of Medicine in Seattle who was not involved in the study.
How Does PPG Work — and Is This New Tech Accurate?
Using PPG, “you’re detecting these small, little blood vessels that sit underneath the surface of your skin,” explained Yang.
“Since both hypertension and diabetes are diseases that damage blood vessels, we thought these diseases might affect blood flow and pulse wave transit times,” said Ryoko Uchida, a project researcher in the cardiology department at the University of Tokyo and one of the leaders of the study.
PPG devices primarily use green light to detect blood flow, as hemoglobin, the oxygen-carrying molecule in blood, absorbs green light most effectively, Yang said. “So, if you extract and remove all the other channels of light and only focus on the green channel, then that’s when you’ll be able to potentially see blood flow and pulsatile blood flow activity,” he noted.
The University of Tokyo researchers used remote or contactless PPG, which requires a short video recording of someone’s face and palms, as the person holds as still as possible. A special sensor collects the video and detects only certain wavelengths of light. Then the researchers developed an AI algorithm to extract data from participants’ skin, such as changes in pulse transit time — the time it takes for the pulse to travel from the palm to the face.
To correlate the video algorithm to blood pressure and diabetes risk, the researchers measured blood participants’ pressure with a continuous sphygmomanometer (an automatic blood pressure cuff) at the same time as they collected the video. They also did a blood A1c test to detect diabetes.
So far, they’ve tested their video algorithm on 215 people. The algorithm applied to a 30-second video was 86% accurate in detecting if blood pressure was above normal, and a 5-second video was 81% accurate in detecting higher blood pressure.
Compared with using hemoglobin A1c blood test results to screen for diabetes, the video algorithm was 75% accurate in identifying people who had subtle blood changes that correlated to diabetes.
“Most of this focus has been on wearable devices, patches, rings, wrist devices,” Yang said, “the facial video stuff is great because you can imagine that there are other ways of applying it.”
Yang, who is also doing research on facial video processing, pointed out it could be helpful not only in telehealth visits, but also for patients in the hospital with highly contagious diseases who need to be in isolation, or just for people using their smartphones.
“People are tied to their smartphones, so you could imagine that that would be great as a way for people to have awareness about their blood pressure or their diabetes status,” Yang noted.
More Work to Do
The study has a few caveats. The special sensor they used in this study isn’t yet integrated into smartphone cameras or other common video recording devices. But Uchida is hopeful that it could be mass-produced and inexpensive to someday add.
Also, the study was done in a Japanese population, and lighter skin may be easier to capture changes in blood flow, Uchida noted. Pulse oximeters, which use the same technology, tend to overestimate blood oxygen in people with darker skin tones.
“It is necessary to test whether the same results are obtained in a variety of subjects other than Japanese and Asians,” Uchida said, in addition to validating the tool with more participants.
The study has also not yet undergone peer review.
And Yang pointed out that this new AI technology provides more of a screening tool to predict who is at high risk for high blood pressure or diabetes, rather than precise measurements for either disease.
There are already some devices that claim to measure blood pressure using PPG technology, like blood pressure monitoring watches. But Yang warns that these kinds of devices aren’t validated, meaning we don’t really know how well they work.
One difficulty in getting any kind of PPG blood pressure monitoring device to market is that the organizations involved in setting medical device standards (like the International Organization for Standards) doesn’t yet have a validation standard for this technology, Yang said, so there’s really no way to consistently verify the technology’s accuracy.
“I am optimistic that we are capable of figuring out how to validate these things. I just think we have so many things we have to iron out before that happens,” Yang explained, noting that it will be at least 3 years before a remote blood monitoring system is widely available.
A version of this article first appeared on Medscape.com.
When a patient signs on to a telehealth portal, there’s little more a provider can do than ask questions. But a new artificial intelligence (AI) technology could allow providers to get feedback about the patient’s blood pressure and diabetes risk just from a video call or a smartphone app.
Researchers at the University of Tokyo in Japan are using AI to determine whether people might have high blood pressure or diabetes based on video data collected with a special sensor.
The technology relies on photoplethysmography (PPG), which measures changes in blood volume by detecting the amount of light absorbed by blood just below the skin.
Wearable devices like Apple Watches and Fitbits also use PPG technologies to detect heart rate and atrial fibrillation.
“If we could detect and accurately measure your blood pressure, heart rate, and oxygen saturation non-invasively that would be fantastic,” said Eugene Yang, MD, professor of medicine in the division of cardiology at the University of Washington School of Medicine in Seattle who was not involved in the study.
How Does PPG Work — and Is This New Tech Accurate?
Using PPG, “you’re detecting these small, little blood vessels that sit underneath the surface of your skin,” explained Yang.
“Since both hypertension and diabetes are diseases that damage blood vessels, we thought these diseases might affect blood flow and pulse wave transit times,” said Ryoko Uchida, a project researcher in the cardiology department at the University of Tokyo and one of the leaders of the study.
PPG devices primarily use green light to detect blood flow, as hemoglobin, the oxygen-carrying molecule in blood, absorbs green light most effectively, Yang said. “So, if you extract and remove all the other channels of light and only focus on the green channel, then that’s when you’ll be able to potentially see blood flow and pulsatile blood flow activity,” he noted.
The University of Tokyo researchers used remote or contactless PPG, which requires a short video recording of someone’s face and palms, as the person holds as still as possible. A special sensor collects the video and detects only certain wavelengths of light. Then the researchers developed an AI algorithm to extract data from participants’ skin, such as changes in pulse transit time — the time it takes for the pulse to travel from the palm to the face.
To correlate the video algorithm to blood pressure and diabetes risk, the researchers measured blood participants’ pressure with a continuous sphygmomanometer (an automatic blood pressure cuff) at the same time as they collected the video. They also did a blood A1c test to detect diabetes.
So far, they’ve tested their video algorithm on 215 people. The algorithm applied to a 30-second video was 86% accurate in detecting if blood pressure was above normal, and a 5-second video was 81% accurate in detecting higher blood pressure.
Compared with using hemoglobin A1c blood test results to screen for diabetes, the video algorithm was 75% accurate in identifying people who had subtle blood changes that correlated to diabetes.
“Most of this focus has been on wearable devices, patches, rings, wrist devices,” Yang said, “the facial video stuff is great because you can imagine that there are other ways of applying it.”
Yang, who is also doing research on facial video processing, pointed out it could be helpful not only in telehealth visits, but also for patients in the hospital with highly contagious diseases who need to be in isolation, or just for people using their smartphones.
“People are tied to their smartphones, so you could imagine that that would be great as a way for people to have awareness about their blood pressure or their diabetes status,” Yang noted.
More Work to Do
The study has a few caveats. The special sensor they used in this study isn’t yet integrated into smartphone cameras or other common video recording devices. But Uchida is hopeful that it could be mass-produced and inexpensive to someday add.
Also, the study was done in a Japanese population, and lighter skin may be easier to capture changes in blood flow, Uchida noted. Pulse oximeters, which use the same technology, tend to overestimate blood oxygen in people with darker skin tones.
“It is necessary to test whether the same results are obtained in a variety of subjects other than Japanese and Asians,” Uchida said, in addition to validating the tool with more participants.
The study has also not yet undergone peer review.
And Yang pointed out that this new AI technology provides more of a screening tool to predict who is at high risk for high blood pressure or diabetes, rather than precise measurements for either disease.
There are already some devices that claim to measure blood pressure using PPG technology, like blood pressure monitoring watches. But Yang warns that these kinds of devices aren’t validated, meaning we don’t really know how well they work.
One difficulty in getting any kind of PPG blood pressure monitoring device to market is that the organizations involved in setting medical device standards (like the International Organization for Standards) doesn’t yet have a validation standard for this technology, Yang said, so there’s really no way to consistently verify the technology’s accuracy.
“I am optimistic that we are capable of figuring out how to validate these things. I just think we have so many things we have to iron out before that happens,” Yang explained, noting that it will be at least 3 years before a remote blood monitoring system is widely available.
A version of this article first appeared on Medscape.com.