Google has announced the first four startups to join Launchpad Studio, its 6-month mentorship program tailored to help applied machine learning startups build great products using the most advanced tools and technologies available. Working side-by-side with experts from across Google product and research teams — including Google Cloud, Verily, X, Brain, ML Research — the program intends to support these startups on their journey to build successful applications, and explore leveraging Google Cloud Platform, TensorFlow, Android, and other Google platforms. Launchpad Studio has also enlisted the expertise of a number of top industry practitioners and thought leaders to ensure Studio startups are successful in practice and long-term.
These four startups were selected based on the novel ways they’ve found to apply machine learning to important challenges in the healthcare industry:
Reducing doctor burnout and increasing doctor productivity (Augmedix)
Augmedix has developed a software for Google Glass to alleviate the documentation and administrative load from doctors, saving them 2-3 hours per day while improving the doctor-patient experience. The company has started leveraging advances in deep learning and natural language understanding to accelerate these efficiencies and offer additional value that further improves patient care.
Regaining movement in paralyzed limbs (BrainQ)
BrainQ is developing a medical device that utilizes artificial intelligence tools to identify high resolution spectral patterns in patient’s brain waves, observed in electroencephalogram (EEG) sensors. These patterns are then translated into a personalized electromagnetic treatment protocol aimed at facilitating targeted neuroplasticity and enhancing patient’s recovery. The company is currently conducting clinical trials in leading hospitals in Israel.
Accelerating clinical trials and enabling value-based healthcare (Byteflies)
Through their medical and signal processing expertise, Byteflies has made advances in the interpretation of multiple synchronized vital signs. This multimodal high-resolution vital sign data is very useful for healthcare and clinical trial applications. With that level of data ingestion comes a great need for automated data processing. Byteflies plans to use machine learning to transform these data streams into actionable, personalized, and medically-relevant data.
Detecting sepsis (CytoVale)
CytoVale is developing a medical diagnostics platform based on cell mechanics, initially for use in early detection of sepsis in the emergency room setting. It analyzes thousands of cells’ mechanical properties using ultra high speed video to diagnose disease in a few minutes. Their technology also has applications in immune activation, cancer detection, research tools, and biodefense. CytoVale is leveraging recent advances in machine learning and computer vision in conjunction with their unique measurement approach to facilitate this early detection of sepsis.
Each startup will get tailored, equity-free support, with the goal of successfully completing a machine learning-focused project during the term of the program. Google also provides resources, including deep engagement with engineers in Google Cloud, Google X, and other product teams, as well as Google Cloud credits. Plus both Google Cloud Platform and GSuite training are included, as well.