New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience.
MIT engineers have developed a telerobotic system to help surgeons quickly and remotely treat patients experiencing a stroke or aneurysm. With a modified joystick, surgeons in one hospital may control a robotic arm at another location to safely operate on a patient during a critical window of time that could save the patient's life and preserve their brain function. The robotic system, whose movement is controlled through magnets, is designed to remotely assist in endovascular intervention -- a procedure performed in emergency situations to treat strokes caused by a blood clot. Such interventions normally require a surgeon to manually guide a thin wire to the clot, where it can physically clear the blockage or deliver drugs to break it up. One limitation of such procedures is accessibility: Neurovascular surgeons are often based at major medical institutions that are difficult to reach for patients in remote areas, particularly during the "golden hour" -- the critical period after a stroke's onset, during which treatment should be administered to minimize any damage to the brain.
Don't worry, yes, there are even more Musk machinations, but first let's broach something a little different -- and possibly lifesaving. A team of MIT engineers is developing a telerobotic system for neurosurgeons. It unveiled a robotic arm that doctors can control remotely using a modified joystick to treat stroke patients. The arm has a magnet attached to its wrist, and surgeons can adjust its orientation to guide a magnetic wire through the patient's arteries and vessels to remove blood clots in the brain. Like in-person procedures, surgeons will have to rely on live imaging to get to the blood clot, but the machine means they don't have to be physically with the patient.
Remote robotic-assisted surgery is far from new, with various educational and research institutions developing machines doctors can control from other locations over the years. There hasn't been a lot of movement on that front when it comes to endovascular treatments for stroke patients, which is why a team of MIT engineers has been developing a telerobotic system surgeons can use over the past few years. The team, which has published its paper in Science Robotics, has now presented a robotic arm that doctors can control remotely using a modified joystick to treat stroke patients. That arm has a magnet attached to its wrist, and surgeons can adjust its orientation to guide a magnetic wire through the patient's arteries and vessels in order to remove blood clots in their brain. Similar to in-person procedures, surgeons will have to rely on live imaging to get to the blood clot, except the machine will allow them to treat patients not physically in the room with them.
With 2021 behind us, we're going down memory lane to highlight biotech innovations that shaped the year--with impact that will likely reverberate for many years to come. Covid-19 dominated the news, but science didn't stand still. CRISPR spun off variations with breathtaking speed, expanding into a hefty toolbox packed with powerhouse gene editors far more efficient, reliable, and safer than their predecessors. CRISPRoff, for example, hijacks epigenetic processes to reversibly turn genes on and off--all without actually snipping or damaging the gene itself. Prime editing, the nip-tuck of DNA editing that only snips--rather than fully cutting--DNA received an upgrade to precisely edit up to 10,000 DNA letters in a variety of cells.
Artificial intelligence (AI) has the potential to play a role in predictive medicine, from prevention and diagnosis to treatment. Machine learning models have proved useful in certain types of leukemia and deep learning in diabetic retinopathy. However, contrary to expectations of human bias removal, evidence has shown an increased bias, and hence unfairness, against specific subpopulations. The problem arises because AI programs learn from data and they will simply learn differently depending on the datasets physicians or researchers employ to train them. A study published in Science (open access) this week investigates the bias in AI models used to predict cognitive, behavioral, and psychiatric patterns that may characterize a disorder. Jingwei Li and collaborators examined whether white Americans and African Americans enjoyed similar predictive performance when the AI models were trained with state-of-the-art large-scale datasets containing neuroimaging and behavioral data.
Seyam's group conducted a study that assessed the effect of incorporating into their emergency department workflow a commercially available deep-learning algorithm (Aidoc) to diagnose intracranial hemorrhage on CT. The team then compared the algorithm's diagnostic performance to preimplementation diagnoses of the condition. The study included 4,450 patients who underwent emergency head CT; of those, 3,017 had exams after the AI algorithm had been implemented.