Validation of AI Lumbar Spine Anatomy Measureing Function

Functionality Assessment of RadiSpine, an Artificial Intelligence Software as Medical Device for Lumbar Spine Quantification System

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06950411

Acronym

Spinal cord

Enrollment

150

Registered

2025-04-30

Start date

2024-09-12

Completion date

2025-03-31

Last updated

2025-04-30

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Spinal Cord Compression

Keywords

Artificial Intelligence(AI) Algorithm Spinal anatomy

Brief summary

Spinal degeneration and its associated clinical diseases are common ailments in aging societies. With the advent of a super-aging society, the importance of assistive technologies for spinal image interpretation is increasingly significant to enhance care efficiency and reduce medical personnel expenditure. Recently, due to the rapid development of artificial intelligence (AI) algorithm, AI-based computer-assisted detection (CADe) devices gradiually become a convenient method for spinal anatomy measurement. However, the accuracy of these devices has not been fully established. This study aims to validate the performance of RadiSpine (an application program) in spinal anatomy segmentation and measurement.

Interventions

None listed

Study design

Observational model

OTHER

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

22 Years to 75 Years

Healthy volunteers

Yes

Inclusion criteria

The subjects should be aged 20 or older and younger than 75, with an equal gender distribution of 50% male and 50% female. From this group, 150 subjects with reasonable datavalues will be selected, with a requirement that at least 30% of them are male and at least 30% are female.

Exclusion criteria

1. With history of spinal surgery 2. Spinal trauma 3. Spinal osteoporosis 4. Spinal metastasis or infection

Design outcomes

Primary

Measure	Time frame	Description
Segmentation accuracy (Mean)	30 mins per individual	The minimum Mean Dice Coefficient (MDC), defined as the lower limit of the 95% confidence interval (CI) for MDC, is above a predetermined allowable limit equal to 0.8

Secondary

Measure	Time frame	Description
Measurement accuracy	30 mins per individual	The maximum Mean Absolute Error (MAE), defined as the upper limit of the 95% CI for MAE, is below a predetermined allowable error limit equal to 2 mm

Countries

Taiwan

Outcome results