Overcoming Critical Challenges
in Musculoskeletal Diagnostics
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Diagnostic Accuracy & Consistency Gaps
- An estimated 3-5% of radiological studies daily may contain diagnostic errors.
- Missed fracture rates for extremities average approximately 3.7%, potentially exceeding 7% in complex anatomical areas like the foot.
- Subtle and gradually developing pathologies (e.g., early osteoarthritis, stress fractures) are particularly challenging to detect consistently, which can delay crucial treatment.
Escalating Workload & Efficiency Pressures
- Continuously rising imaging volumes place significant strain on radiology departments.
- Across Europe, many countries report radiologist numbers below the EU average (approx. 127 per million), compounded by an aging workforce where ~45% are over age 51.
- These pressures contribute to radiologist burnout rates reported at over 50% and can lead to significant reporting delays (e.g., NHS England reported nearly 750,000 imaging tests taking longer than the 4-week target in one year).
Complexity in Tracking Gradual Changes
Manually comparing serial X-rays to monitor disease progression or healing is inherently time-consuming and susceptible to inter-observer variability, making it difficult to reliably detect minute changes.
Critical Issues in MSK Diagnostics
Identifying and addressing key challenges in musculoskeletal imaging and interpretation.
Key percentages in radiology statistics with risk indicators highlighted.
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AKinetic Scan Your Intelligent Partner in Radiological Assessment
Kinetic Scan is an innovative AI platform utilizing advanced Deep Learning algorithms. We empower clinicians by significantly enhancing diagnostic accuracy and efficiency for common and challenging conditions identified via spine and leg X-rays. We help you see more, faster, with greater confidence
Enhanced Diagnostic
Accuracy
- Significantly improves the detection of subtle and hard-to-find conditions.
- In our pilot studies (600+ X-rays), Kinetic Scan identified potential abnormalities missed in standard initial reads in approximately 12% of complex cases
- For specific challenging findings, such as subtle spondylolysis, the system demonstrated an improved detection rate of around 20% compared to initial reads by less experienced staff
- Our AI models achieved an average sensitivity of approximately 92% and specificity of around 88% (AUC >0.93) on pilot datasets for targeted pathologies.


Increased Workflow
Efficiency
- Reduces interpretation times for complex cases, directly addressing workload pressures.
- Customizable Alert System: “Clinicians can opt for automated email notifications for specific findings, tailored to workflow priorities.”
- Our pilot studies showed an average 18% reduction in interpretation time for complex spine and leg X-ray series.
- Our AI models achieved an average sensitivity of approximately 92% and specificity of around 88% (AUC >0.93) on pilot datasets for targeted pathologies.
Reliable Longitudinal
Monitoring
- Objectively and consistently tracks disease progression (like osteoarthritis) or healing over serial X-rays.
- In testing, the platform consistently detected subtle interval changes (e.g., differences <0.5mm in joint space narrowing) in approximately 85% of the longitudinal osteoarthritis cases assessed.


Unique Integrated Prosthetic
Design Capability
- Offers an innovative ‘diagnosis-to-design’ pathway, automatically generating validated, high-quality 3D designs for patient-specific prosthetics directly from the X-ray analysis.
- This streamlines custom prosthetic development and facilitates access to personalized solutions, especially for local, low-cost 3D printing.
What Our Clinical Partners about us
Protect the dignity of those receiving care and to enhance the quality of
life for both seniors and their families.





