machine learning Archives - Page 3 of 5

PsychoGenics’ SmartCube prompts a reevaluation of CNS drug discovery

SmartCube integrates behavioral neurobiology, robotics and computer vision to process and analyze large temporal and vector-based datasets. This platform uses proprietary bioinformatics and probabilistic causal inference algorithms to explore compounds’ potential to treat psychiatric disorders. [Image courtesy of PsychoGenics]

In an era of rapid AI progress, the quest to pioneer the first AI-developed drug candidates has led to an increasing number of these drug candidates entering clinical trials. One contender is ulotaront, an antipsychotic drug, that fared well in a phase 3 schizophrenia study published in NEJM in 2020. The trace amine-associated receptor 1 agonist has entered phase 2/3 studies to test its potential in generalized anxiety disorder and as an adjunctive therapy for major depressive disorder treatment.

“Not only is this the first drug discovered using machine learning that’s th…

A current perspective on machine learning’s role in advancing clinical trials diversity

[Image courtesy of Adobe Stock]

The year 2020 was a watershed moment for many reasons, but notably, it cast a light on the pervasive health and social inequities that have long marred the U.S. The COVID-19 pandemic hit diverse populations disproportionately hard, as Deloitte and others have noted. Additionally, the tragic deaths of George Floyd, Breonna Taylor and others provoked an uproar over systemic racism that permeates society, including healthcare. This period of societal upheaval has also underscored the necessity of novel approaches involving techniques like the use of machine learning in clinical trials, to ensure that diverse populations are represented.

Such disparities in healthcare were further highlighted when Moderna, soon to become a critical player in the vaccine race, faced a glaring revelation in late 2020. Only 24% of participants in their phase 3 study were from communities of color, despit…

Codagenix taps synthetic biology and machine learning in vaccine development

[Image courtesy of Production Perig/Adobe Stock]

In the quest to outsmart viral foes such as SARS-CoV-2, RSV and influenza, Codagenix, a clinical-stage biotech firm based in Farmingdale, New York, has engaged a unique arsenal: the intersection of synthetic biology and machine learning. Their weapon of choice is a blend of live-attenuated virus design and codon deoptimization technology. Their process involves introducing mutations or ‘bad codons’ into the virus to slow its replication rate in the human host, thus converting it from a deadly pathogen into a live attenuated vaccine.

A machine learning-aided algorithm meticulously guides this process, ensuring the modifications are safe and effective. “We recode the DNA of a virus to slow down the rate of translation in the human host cell,” said J. Robert Coleman, the co-founder and CEO of Codagenix.

All viruses use the body to replicate t…

How EEG and machine learning are transforming epilepsy clinical trials

[Image courtesy of peterschreiber.media/Adobe Stock]

Epilepsy is a brain disorder that triggers recurring seizures. It is the fourth the most common neurological disorders in the world, according to the Epilepsy Foundation. The Centers for Disease Control and Prevention estimates that 65 million people worldwide have active epilepsy. In 2015, 1.2% of the total U.S. population — 3 million adults and nearly 500,000 children — had active epilepsy.

There are many different causes of epilepsy, including genetics, head trauma, brain abnormalities, infection, prenatal injury and developmental disorders, such as autism.

Seizure symptoms vary greatly and can manifest in a person as uncontrollable limb movements, staring, muscle stiffness, confusion and loss of consciousness or awareness.

These symptoms are not mutually exclusive. Some patients with epilepsy experience multiple types of seizures.

Epil…

Avail Medsystems plans to roll out first third-party software integration

The Avail Medsystems console brings remote experts into the operating room virtually. [Photo courtesy of Avail Medsystems]

Avail Medsystems is testing a third-party software integration that it plans to launch in the coming months.

Avail executives said they hope it will be the first of many partnerships with medical technology developers of software or algorithms that could be useful in the operating room.

Avail CEO Daniel Hawkins expects to launch the software integration in the first quarter of 2023, he told Medical Design & Outsourcing after a panel discussion at DeviceTalks West in Santa Clara, California. He declined to name his company’s partner, but offered some details as the product goes through UI and customer experience testing.

“A remote user can pull up that software and optimize the workflow in a specialty category that really benefits from workflow enhancement,̶…

Avail Medsystems plans to roll out first third-party software integration

The Avail Medsystems console brings remote experts into the operating room virtually. [Photo courtesy of Avail Medsystems]

Avail Medsystems is testing a third-party software integration that it plans to launch in the coming months.

Avail executives said they hope it will be the first of many partnerships with medical technology developers of software or algorithms that could be useful in the operating room.

Avail CEO Daniel Hawkins expects to launch the software integration in the first quarter of 2023, he told Medical Design & Outsourcing after a panel discussion at DeviceTalks West in Santa Clara, California. He declined to name his company’s partner, but offered some details as the product goes through UI and customer experience testing.

“A remote user can pull up that software and optimize the workflow in a specialty category that really benefits from workflow enhancement,̶…

FDA and VA Ventures Innovation Institute partner on new medical device testing tools

Dr. Beth Ripley is the medical device testing collaboration’s project manager for the VA. [Photo courtesy of the Department of Veterans Affairs]

The Department of Veterans Affairs’ VA Ventures Innovation Institute in Seattle will host up to a dozen FDA staffers in a new collaboration to develop and share new tools for medical device testing safety and efficacy.

The agencies intend to provide developers of devices and new technology with off-the-shelf tools that can help streamline regulatory review and get products to patients faster. FDA staff from the Center for Devices and Radiological Health (CDRH) will focus on regulatory science. Their partners at the VA’s Veterans Health Administration (VHA) will provide clinical context for test development and provide hands-on training and other immersive experiences for innovators wishing to utilize the tools.

RELATED: FDA issues draft guidanc…

Researchers develop at-home device to track Parkinson’s progression

An at-home monitoring system could help monitor Parkinson’s progression and response to medication. [Image by N.Fuller/SayoStudio]

Researchers at MIT and the University of Rochester Medical Center have developed a device to monitor a Parkinson’s patient at home.

Collecting data at home could help provide a less subjective measurement than evaluations at the doctor’s office, which can be affected by outside factors. The technology could also help pharmaceutical and biotech companies developing new therapies for Parkinson’s, which is the world’s fastest-growing neurological disease.

In a one-year study with 50 patients, machine learning algorithms analyzed the data to help clinicians track Parkinson’s progression and medication response more effectively than periodic evaluations in a medical facility, MIT said in a news release. The study fund that gait speed declined ne…

Mayo Clinic develops AI childbirth risk prediction tool for women in labor

Dr. Abimbola Famuyide is a Mayo Clinic OB-GYN studying AI algorithms for childbirth. [Photo courtesy of Mayo Clinic]

Mayo Clinic researchers are using AI algorithms to calculate childbirth risk while women are in labor in an effort to reduce the rate of cesarean delivery and complications.

The machine learning algorithms — a type of device known as Software as a Medical Device (SaMD), where the software is the device rather than a mechanical device — analyze patterns of changes for women in labor.

ADVICE: How to pass the patent eligibility test for Software as a Medical Device

“This is the first step to using algorithms in providing powerful guidance to physicians and midwives as they make critical decisions during the labor process,” senior author Dr. Abimbola Famuyide said in a news release. “Once validated with further research, we believe the algorithm will work in real time,…

Blue Spark’s TempTraq catches fevers faster. Fever prediction is next.

Blue Spark’s TempTraq continuous temperature monitoring patch could one day be used for fever prediction. The patch is 100mm long, 50mm wide, 3mm thick and weighs 5.1 grams. [Photo courtesy of Blue Spark Technologies]

Blue Spark Technologies developed the first wireless continuous temperature monitor patch, TempTraq, to enable faster fever detection than standard manual readings every four hours.

Westlake, Ohio-based Blue Spark is now looking at fever prediction rather than just detecting them.

The R&D team is working on developing an AI neural network model built on the company’s collection of continuous temperature data captured in the cloud, CEO John Gannon said in an interview.

“Taking our existing data, training an AI model to be able to understand and learn what fever profiles and onsets of fevers look like, and then apply that to new patients … we are working tow…

Accelerating R&D with FAIR data

[Photo by Chokniti Khongchum on Pexels]

In 2016, the FAIR Guiding Principles for scientific data management and stewardship were published, laying out guideposts for scholarly data producers to make their data discoverable and usable in the future. The FAIR principles seek to ensure that data is Findable, Accessible, Interoperable, and Reusable. At the time of their publication, they articulated and centralized many points of discussion among data scientists, and since then, they have been widely referenced and utilized in academia.

Of course, collaboration and data reuse are fundamental to academic research. But the ideas of FAIRness can actually bring value to any research organization, including life sciences companies with vast archives of proprietary data. For example, a pharma company may expect that its data will always stay within its own walls, but its effective management and reuse can have dramatic …

AI-enabled 3D printer watches and improves material handling on the fly

Two computer vision cameras monitor this 3D printer to detect errors and adjust in real time. [Photo courtesy of MIT]

An AI-enabled additive manufacturing system allowed a 3D printer to monitor and adjust material handling in real time, cutting down the trial-and-error process of learning how to print with new materials.

The technology could make it easier for engineers in medtech and other industries to use new materials in 3D printed products for special electrical or chemical properties, cutting down on the time and materials spent on trial-and-error. It could also help manufacturing equipment adjust to changes in the material or printing environment.

The MIT researchers behind the research project said it could have applications for manufacturing processes beyond 3D printing.

“This project is really the first demonstration of building a manufacturing system that uses machine learning to learn …