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RS85 Diagnostic Ultrasound System

Samsung Medison Co., Ltd. • Radiology • 06/12/2024

510(k)IEC 62304 Class B
FDA Summary PDF

Device Purpose

The RS85 Diagnostic Ultrasound System is a comprehensive, mobile imaging platform designed to acquire and display ultrasound data across a multitude of clinical applications using various established and advanced imaging modes. It enhances diagnostic capabilities through features like AI-powered NerveTrack for improved nerve visualization and quantitative measurement, alongside improved hardware such as an OLED monitor and additional transducer options.

Summary

The RS85 Diagnostic Ultrasound System is a general-purpose, mobile, software-controlled device designed for acquiring ultrasound images and analyzing body fluids. It is intended for use by appropriately trained healthcare professionals in various clinical settings, including hospitals and private practices, for diagnostic purposes. The system supports a wide array of clinical applications such as fetal/obstetrics, abdominal, gynecological, intraoperative, pediatric, small organ, cephalic (neonatal and adult), trans-rectal, trans-vaginal, musculoskeletal, urological, cardiac (adult and pediatric, including trans-esophageal), and peripheral vessel imaging. It offers multiple modes of operation including 2D, M-mode, Color Doppler, Pulsed Wave (PW) Doppler, Continuous Wave (CW) Doppler, Tissue Doppler Imaging (TDI), Tissue Doppler Wave (TDW), Power Doppler (PD), ElastoScan™ Mode, Multi-Image modes, and 3D/4D imaging. The system incorporates several technological advancements and features. These include two migrated transducers (miniER7, L3-22), an Extended Power Pack (EPP) for additional power supply, and a 27-inch OLED monitor. A key feature is NerveTrack, an AI-based technology for nerve detection and segmentation, which includes a sub-function, EzNerveMeasure, for calculating Flattening Ratio (FR) and Cross-sectional Area (CSA) of nerves. Non-clinical validation of NerveTrack's detection algorithm on 3,999 nerve images showed an average accuracy of 89.6%. The segmentation algorithm, validated on 1,753 images, demonstrated an average accuracy of 99.78% and a Dice similarity coefficient of 90.44%. EzNerveMeasure, tested on 50 median nerve images, showed average error rates of 6.11% for FR and 9.75% for CSA. The system also features SonoSync for remote diagnostic image viewing and review, and Mobile Export as an optional feature.

AI Regulatory Concerns

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

Keywords

RS85 Diagnostic Ultrasound SystemNerveTrackAI TechnologyUltrasound ImagingDopplerElastoScan3D/4D modeEzNerveMeasureSonoSyncMobile Export

Device Metadata

Submission Number
K240516
Company
Samsung Medison Co., Ltd.
Panel
Radiology
Product Code
N/A
Data Type
Images
Clinical Function
Assessment
AI Function
Analysis
AI Subclass
quantification/feature localization

Relevant Clinical Questions

  • Q1. How does the performance of NerveTrack's AI-based detection and segmentation vary across different nerve types and patient demographics beyond the Korean population used in the validation studies?; What is the quantifiable impact of the EzNerveMeasure feature (Flattening Ratio and Cross-sectional Area) on diagnostic accuracy and inter-observer variability for conditions like carpal tunnel syndrome?; How does the image quality and diagnostic utility of the 27-inch OLED monitor compare to standard medical-grade LCDs, particularly in visualizing subtle tissue differentiations or low-contrast lesions?; What are the specific network requirements and latency considerations for the SonoSync remote viewing feature to ensure diagnostic integrity and real-time collaborative utility?; How do the new transducers (miniER7, L3-22) enhance imaging capabilities for their intended applications compared to previously available transducers for this system or its predicates?

Risk Classification

ESL Analysis
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

Powered by SBOMator™

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 Score15/100
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)
✓ Detected
SaaSBOMSoftware-as-a-Service Bill of Materials(server, remote, network, platform)
✓ Detected
HBOMHardware Bill of Materials(hardware)
✓ 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, SaaSBOM, HBOM 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, CBOM, VDR/VEX, OBOM. These may be present in the full FDA submission.

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RS85 Diagnostic Ultrasound System - Samsung Medison Co., Ltd. | ESL FDA AI Device Intelligence