During a clinical trial, accurate assessments of a patient’s response to treatment are critical to measure the state of their disease and a drug’s impact. However, traditional measures of a patient’s wellbeing, including sporadic in-person assessments or self-reported outcomes, only capture a single moment in time for that individual. The stretches between these check-ins can tell an entirely different story, filled with potential insights about a person’s condition, response to therapy, or degree of engagement in the clinical trial.
AI-powered digital biomarkers deployed on phone-based platforms may allow sponsors to remotely capture these individual patient responses – from changes in facial characteristics, vocal patterns, movement and more – offering a unique and objective view of disease progression over time. Despite this potential, the proprietary nature of many digital biomarker algorithms renders them inaccessible to the scientific community, hindering their validation and improvement. AiCure’s open-source platform helps to break down these barriers, foster collaboration, and allow trust for novel measurements to be built in the public domain. Democratizing access to these algorithms and welcoming diverse perspectives across the industry to contribute to their development can open a world of possibilities in understanding the nuances of patient behavior, and driving equitable, personalized care.
The power of digital biomarkers
The potential impact of digital biomarkers can best be explained through an example. Imagine someone who has recently been diagnosed with depression and has started therapy with any of a number of available medications. For several weeks on initiation of therapy, they may start experiencing effects of the medication such as sedation that negatively impact their quality of life. When they visit their doctor for a check-up, they may find it difficult to disentangle these effects from the symptoms of their disease. This is not only a highly frustrating experience for patients, but can negatively impact the ability of doctors to efficiently optimize an individual’s medication and dosing.
Instead of relying on subjective self-reported perceptions of how a patient is feeling, digital measures collected on phones can treat these assessments more like an engineering problem, measuring a patient’s response using computer vision algorithms applied to video and audio collected remotely. Especially for conditions with symptoms that have visual and auditory characteristics such as schizophrenia or Parkinson’s, an objective, consistent way to track a patient’s response can help elevate a trial’s data and accelerate research. Through AI-powered analysis of patient video and audio data, clinicians and researchers can pinpoint these crucial disease characteristics to personalize a patient’s care and better understand a patient’s lived experience with their illness.
While the future of this innovation is exciting, it’s often under lock and key as proprietary technology. The reality is that the quality development of these solutions is an endeavor that no one company can take on alone. Open-source AI platforms that bring together diverse industry perspectives and data are vital to making digital biomarker technology a part of everyday research and patient care.
