BoneMetrics (US)

Gleamer SAS • Radiology • 02/05/2025

510(k)IEC 62304 Class B

Device Purpose

The BoneMetrics (US) device is an AI-powered medical image processing system designed to automatically measure Cobb angles on frontal spine radiographs, assisting healthcare professionals in the assessment of spinal deformities like scoliosis by accurately identifying vertebral keypoints. This Class II device utilizes a validated AI algorithm to process images and return annotated results, demonstrated through clinical studies to reliably meet predefined performance criteria for Cobb angle measurements.

Summary

The BoneMetrics (US) device, developed by Gleamer SAS, has been evaluated and determined to be substantially equivalent to legally marketed products. Classified as a Class II medical image management and processing system, this device is designed to assist healthcare professionals. Its primary function is to provide fully automated measurements of Cobb angles on frontal spine radiographs for individuals aged four years and older, specifically for patients with suspected or present spinal deformities such as scoliosis. It is emphasized that the device should serve as an aid and not replace comprehensive patient evaluation or be the sole basis for diagnosis.This software device operates by receiving radiographs, automatically processing them using an AI algorithm to identify vertebral keypoints, and calculating Cobb angles, displaying values above 7 degrees. The processed results, including annotated images and angle measurements, are then sent back in DICOM format for review by the user, typically via a PACS system. Nonclinical and clinical performance tests were conducted to validate the device's accuracy. A clinical standalone performance study on a dataset of frontal spine radiographs, involving expert radiologists and an orthopedic surgeon for ground truth establishment, demonstrated that BoneMetrics (US) consistently met its predefined performance acceptance criteria for Cobb angle measurements, confirming its reliability and effectiveness.

AI Regulatory Concerns

Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

Keywords

BoneMetrics (US)Cobb anglesSpinal deformitiesScoliosisAI algorithmRadiographsVertebral keypointsMedical image processingClinical validationDICOM

Device Metadata

Submission Number

K241593

Company

Gleamer SAS

Panel

Radiology

Product Code

N/A

Data Type

N/A

Clinical Function

N/A

AI Function

N/A

AI Subclass

N/A

Relevant Clinical Questions

Q1. What is the sensitivity and specificity of the BoneMetrics (US) device in detecting scoliosis compared to manual measurements by various expert levels?; How does the device's performance vary across different radiograph qualities, patient age groups (especially near the 4-year minimum), or types of spinal deformities beyond typical scoliosis?; Are there any known limitations or specific types of spinal anomalies where the AI algorithm might struggle or produce inaccurate Cobb angle measurements?; What is the clinical workflow impact and time-saving benefit of integrating BoneMetrics (US) into existing PACS systems in a busy clinical setting?; Has there been any long-term follow-up or studies on the device's consistency and accuracy in tracking progression or regression of spinal deformities over time?

Risk Classification

ESL Analysis

IEC 62304 ClassB

FDA Device ClassII

FDA Class II via 510(k) pathway; IEC 62304 Class B (software safety classification); Post-§524B but no clear network capability detected

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Cybersecurity Evidence

Based on AI-generated device summaries, not the full FDA submission. Items not detected here may still be present in the complete submission package.

Cyber Score5/100

SBOM Evidence⚠ Not in summary - likely in full submission per §524B

Cyber Risk Assessment⚠ Not in summary - likely in full submission per §524B

Postmarket Plan⚠ Not in summary - likely in full submission per §524B

§524B ApplicableYes

Supply Chain BOM Evidence

Based on the CycloneDX / OWASP ECMA-424 xBOM ecosystem. Detection is from the AI-generated summary.

SBOMSoftware Bill of Materials

- Not detected

ML-BOMMachine Learning Bill of Materials(algorithm, dataset, ground truth)

✓ Detected

SaaSBOMSoftware-as-a-Service Bill of Materials

- Not detected

HBOMHardware Bill of Materials

- Not detected

CBOMCryptography Bill of Materials

- Not detected

VDR/VEXVulnerability Disclosure Report / Vulnerability Exploitability eXchange

- Not detected

OBOMOperations Bill of Materials

- Not detected

Analysis:

• Analysis is based on the AI-generated device summary, not the full FDA submission package. Click "Analyze Full FDA PDF" below to scan the actual submission document.

• Evidence of ML-BOM detected in AI-generated device summary.

• SBOM not mentioned in AI-generated device summary. Under §524B (effective Oct 2023), cyber devices must provide a SBOM.

• Cybersecurity risk assessment not referenced in AI-generated device summary. FDA cybersecurity guidance (2023) expects this for cyber devices.

• Postmarket cybersecurity monitoring plan not clearly documented in AI-generated device summary.

• Not detected in AI-generated device summary: SBOM, SaaSBOM, HBOM, CBOM, VDR/VEX, OBOM. These may be present in the full FDA submission.

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