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April 22, 2022

FSHD Society to enhance trial design with AI technology deployment

The FSHD CTRN will collect the dataset in the Clinical Trial Readiness to Solve Barriers to Drug Development in FSHD study.

The FSHD Society is set to improve clinical trial design for facioscapulohumeral muscular dystrophy (FSHD) by deploying Artificial Intelligence (AI) technology.

It has announced the launch of a partnership with the FSHD Clinical Trial Research Network (CTRN) and BullFrogAI for analysing the natural history dataset obtained from FSHD patients.

The debilitating, genetic disease FSHD causes life-long, progressive muscle weakness.

Under the partnership, BullFrogAI will analyse a large and multidimensional FSHD clinical dataset by leveraging the bfLEAP AI platform.

BullFrogAI’s bfLEAP analytics engine is derived from technology that was developed at the Johns Hopkins University Applied Physics Laboratory (APL) and helps to evaluate complex, multi-factorial data sets.

The FSHD CTRN will collect the dataset in the Clinical Trial Readiness to Solve Barriers to Drug Development in FSHD (ReSolve; NCT03458832) study funded by NIH.

A total of 220 patients from eight US and three European CTRN sites took part in the trial, where multiple motor outcome instruments were used for monitoring disease progression over a period of 24 months.

They included an FSHD Functional Composite, Reachable Workspace, the Motor Function Measure Domain 1, and electrical impedance myography.

FSHD Society chief science officer Jamshid Arjomand said: “We hope these studies will help with clinical trial design and patient stratification to accelerate clinical trials for our community.”

The AI analysis helps to evaluate the outcome measures, or combination of measures that are sensitive to changes associated with disease progression.

FSHD CTRN co-director Jeffrey Statland said: “This collaboration can set the stage for more efficient clinical trial design, and can serve as a model for collaborations with AI companies to evaluate large clinical trial preparedness data sets.”

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