Our AI Impact

 for the health of people



 Our AI Impact

 for the health of planet



 Our AI Impact

 for the health of business



“The work [with Synaptiq] is unprecedented in its scale and potential impact,” Mortenson Center’s Managing Director Laura MacDonald MacDonald said. “It ties together our center’s strengths in impact evaluation and sensor deployment to generate evidence that informs development tools, policy, and practice.” 
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    ⇲ Implement & Scale
    A startup in digital health trained a risk model to open up a robust, precise, and scalable processing pipeline so providers could move faster, and patients could move with confidence after spinal surgery. 
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      Thwart errors, relieve in-take form exhaustion, and build a more accurate data picture for patients in chronic pain? Those who prefer the natural albeit comprehensive path to health and wellness said: sign me up. 
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        Using a dynamic machine vision solution for detecting plaques in the carotid artery and providing care teams with rapid answers, saves lives with early disease detection and monitoring. 
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          This global law firm needed to be fast, adaptive, and provide unrivaled client service under pressure, intelligent automation did just that plus it made time for what matters most: meaningful human interactions. 
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            Mushrooms, Goats, and Machine Learning: What do they all have in common? You may never know unless you get started exploring the fundamentals of Machine Learning with Dr. Tim Oates, Synaptiq's Chief Data Scientist. You can read and visualize his new book in Python, tinker with inputs, and practice machine learning techniques for free. 

            Start Chapter 1 Now ⇢ 


              How Should My Company Prioritize AIQ™ Capabilities?





                Start With Your AIQ Score

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                  AI SERVICE
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                  DATA STRATEGY


                  Analyzing Spinal Reherniation Risk with a Machine Learning & Machine Vision MVP that Passes the Test

                  See how empowering an entire organization with machine learning and machine vision technologies, training a risk model, opened a robust processing pipeline so providers could move faster, but patients could move with confidence after spinal surgery.


                  Spine by Design (SBD), a startup in digital health, faced the daunting challenge of accurately evaluating reherniation risks for patients undergoing spine surgery. The existing process was time-consuming and error-prone. It jeopardized patient safety and inflated healthcare costs. SBD needed a precise, scalable solution to assess reherniation risks using patient demographics and spinal imaging data.

                  Speed, accuracy, repeatability-what else are you risking? 

                  Machine vision and machine learning (ML) models have humans beat on speed, accuracy, and repeatability. There is even a machine learning technique that quantifies how confident it is in its own model's predictions. Think of it as a friend that removes the nervous sweat bead from your forehead when tough decisions are on the table. So when a company needs to assess risk, ML models go a long way in narrowing the window of probability. This means making more informed decisions, achieving better outcomes, automating time-consuming tasks, lowering costs can all be within reach. Streamlining AI technologies like these also allows human leaders to step back and focus on the end-to-end impact and timeliness of risk assessment from a macro view, instead of getting lost to the pressure of our risk assessor sweat beads. It's probably time to consider what you are risking if your decisions are not data-driven. Hopefully not somebody's spine.


                  Synaptiq's solution harnessed advanced machine vision algorithms to predict reherniation risks for providers to review with their patients. This scalable solution delivered rapid results. Its unique features encompassed a DICOM processing tool, an optimized inference pipeline, and a retraining pipeline to facilitate model enhancements.


                  SBD is now rigorously testing their MVP machine vision solution with researchers at esteemed institutions such as Emory, Rush, and Johns Hopkins. The collaboration aims to further validate the solution's effectiveness in reducing reherniation risks and improving patient outcomes. Healthcare providers can anticipate significant cost savings and enhanced operational efficiency, positioning the MVP as a promising step towards revolutionizing spine surgery and patient care.

                  HUMANKIND OF IMPACT

                  3,000,000 sufferers of herniated discs now have access to next-step-risk options from both doctor and machine.


                  9,000 more people each year will be able to start their new careers in America.

                  HUMANKIND OF IMPACT


                  AI IS HOW WE DO IT,


                  is why we do it