News & numbers “The greatest opportunity offered by AI is not reducing errors or workloads, or even curing
cancer: it is the opportunity to restore the precious and time-honoured connection and trust.” Eric Topol, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
Robotic spine twins
Researchers in the US have created a novel robotic replica of a human spine to enable surgeons to preview the effects of surgical intervention for degenerative disc disease. The 3D printed spinal ‘twins’ are patient- specific and include an artificial disc implant outfitted with a soft magnetic sensor array to assess the viability of cervical disc implant. The method was created by Florida Atlantic University’s Erik Engeberg, senior author of the study, and researchers from the university’s College of Engineering and Computer Science, in collaboration with Frank Vrionis, senior author of the study and director of the Marcus Neuroscience Institute, part of Baptist Health. “This new approach has a powerful potential to enable surgeons to preview and compare the effects of different surgical interventions,” said Vrionis. “The novel system could help in determining whether a constrained, semi-constrained,
or unconstrained device could be the best fit or even a fusion device.”
The patient-specific robotic spine model was based on a CT scan of the human spine. A robotic arm flexed and extended the cervical spine replica while the intervertebral loads were monitored with the soft magnetic sensor array to classify the spine posture with four different machine-learning algorithms. Researchers then compared the capabilities of the algorithms to classify five different postures of the human spine robotic replica (centre, mid-flexion, flexion, mid-extension and extension). Results of the study conducted to assess the viability of using spinal twins were published in the journal Sensors, and showed that the soft magnetic sensor array system had the high capability to classify the five different postures – which can be a predictor of different problems that people experience – with 100% accuracy.
Smartwatch app detects fibrillation
A smartwatch app has successfully detected atrial fibrillation (AFib) with almost 94% accuracy in a study with 2.8 million participants. Over the course of four years, 12,244 users received a notification of suspected AFib. Among 5,227 people who chose to follow up with a clinician, the disease was confirmed in 93.8% of patients using standard diagnostic tools, including clinical evaluation, an electrocardiogram and Holter monitoring. “Digital technologies make it possible to increase general awareness about AFib and its risk factors as well as to improve prevention of AFib and its complications,” said the study’s lead author Yutao Guo, MD, professor of internal medicine at Chinese PLA Medical School and Chinese PLA General Hospital in Beijing. “With the global surge of wearable technology for AFib screening, especially in the challenging setting of the Covid-19 pandemic, the present study provides a possible solution to help people identify possible signs of AFib and get diagnosed and treated earlier.”
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The size of the study was so large because the researchers tracked people who downloaded an AFib screening app on a compatible Huawei smart device. The device uses photoplethysmography to monitor the wearer’s pulse and the app applies an algorithm to detect when the heart rhythm is abnormal. If an abnormal rhythm was detected, the wearer would be contacted by a clinician to set up an appointment for a clinical assessment. One limitation of the study noted by Guo is that only 53.3% of those who received a notification of suspected AFib effectively followed up with a clinician for further evaluation, meaning researchers were unable to verify whether the remaining individuals had clinically confirmed AFib or determine why they did not follow up. While the study used Huawei devices, the researchers also noted that other smart devices like Apple Watch or FitBit that have similar photoplethysmography technology can also be used to measure a person’s pulse and detect abnormalities.
AI physio tool
Researchers in Singapore have developed a machine-learning based motion capture technology that can aid doctors and physiotherapists in their consultations and diagnoses. Developed by a team of researchers, engineers, and data specialists at the Rehabilitation Research Institute of Singapore (RRIS), Precise Marker-less can provide 3D anatomical bone landmark locations with an accuracy of 10–15mm – about the width of an adult’s little finger. Dr Prayook Jatesiktat, research fellow at the Rehabilitation Research Institute of Singapore (RRIS) who led the development of the technology, said: “Our tool could be applied by professionals in several fields. For example, doctors and therapists could potentially use it to objectively analyse their patients’ movements.
“The technology could also benefit athletes and coaches, as it gives them access to an easy-to-use motion capture system to assess their sports-related actions.” Traditionally, motion capture technologies use a marker-based system, where reflective markers attached to a subject’s body allow cameras to reconstruct the movement in 3D. But these physical markers prevent the system from being applied in many healthcare and commercial settings, as their use is time-consuming and requires a well-trained person to attach markers to the subject. Precise Marker-less eliminates the need for these markers as it has ‘learned’ where they would be placed by capturing and analysing the movements of more than 150 subjects. The researchers of the study claim that cutting out the need for a long setup time and specialist expertise would allow patients to start consultations within minutes. It would also enable consultation times to be reduced by at least an hour on average.
RRIS was founded in 2016 by Nanyang Technological University, Singapore, the Agency for Science, Technology and Research and the National Healthcare Group.
12.1 million The estimated number of Americans that
will have atrial fibrillation in 2030. Source: CDC
Medical Device Developments /
www.nsmedicaldevices.com
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