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Analyzing Spinal Reherniation Risk with a Machine Learning & Machine Vision MVP that Passes the Test
Discover how we harnessed machine vision and machine learning to establish a robust data processing pipeline, enabling providers to move faster and patients to move with confidence after spinal surgery.
Problem:
Spine by Design (SBD), a digital health startup, needed to accurately evaluate 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, and 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. It's probably time to consider what you are risking if your decisions are not data-driven. Hopefully not somebody's spine.
See if ML or machine vision could work for your organization →
Solution:
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.
Outcome:
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.
AI IS HOW WE DO IT,