K Number
K242515
Device Name
uDR 380i Pro
Date Cleared
2024-10-10

(48 days)

Product Code
Regulation Number
892.1720
Panel
RA
Reference & Predicate Devices
AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
Intended Use

uDR 380i Pro is a mobile digital radiography device intended to acquire X-ray images of the human anatomy for medical diagnosis. uDR 380i Pro can be used on both adult and pediatric patient by a qualified and trained operator. This device is not intended for mammography.

Device Description

uDR 380i Pro is a diagnostic mobile x-ray system utilizing digital radiography (DR) technology. It can be moved to different environments for an examination, like emergency room. ICU and ward. It mainly consists of a lifting column - telescopic cantilever frame system, system motion assembly, X-ray System (high voltage generator, x-ray tube, collimator and wireless flat panel detectors which have been cleared in K230175), power supply system and software for acquiring and processing the clinical images.

AI/ML Overview

The provided text is a 510(k) summary for the uDR 380i Pro mobile X-ray system. This document primarily focuses on establishing substantial equivalence to a predicate device (K222339) and does not contain detailed information about acceptance criteria or a comprehensive study demonstrating the device's performance against specific acceptance criteria.

The key change in this 510(k) submission is the addition of new flat panel detectors (CXDI-710C and CXDI-810C) and associated control software (CXDI Control Software NE). The document explicitly states: "The modifications performed on the uDR 380i Pro (K222339) in this submission are due to the addition of flat panel detectors, including CXDI-710C and CXDI-810C, and CXDI Control Software NE which were cleared in K230175." and "The device software is unchanged from the predicate device, except for the addition of CXDI Control Software NE."

Therefore, the performance data provided is primarily to demonstrate that these new components do not adversely affect the safety and effectiveness or alter the fundamental scientific technology of the device compared to the predicate.

Here's an analysis of the provided information concerning acceptance criteria and study details:

1. A table of acceptance criteria and the reported device performance:

The document does not present a formal table of "acceptance criteria" for the device's overall performance. Instead, it compares the technological characteristics of the proposed device to the predicate device in Table 1: Comparison of Technology Characteristics. This table implicitly defines the acceptance (or "sameness") criteria based on the predicate device's specifications.

ITEMPredicate Device: uDR 380i Pro (K222339)Proposed Device: uDR 380i ProRemark
Product CodeIZLIZLSame
Regulation No.21 CFR 892.172021 CFR 892.1720Same
ClassIIIISame
Indications UseMobile digital radiography device for X-ray images of human anatomy for medical diagnosis for adult and pediatric patients. Not for mammography.Mobile digital radiography device for X-ray images of human anatomy for medical diagnosis for adult and pediatric patients. Not for mammography.Same
Specifications (Selected)
DQE (Flat Panel Detector)Typical: 58% @3uGy,0.5lp/mmTypical: 0.58±10% @3uGy,0.5lp/mm (for AR-C3543W&AR-B2735W), Typical: 0.58±10% @2.5uGy,0.5lp/mm (for CXDI-710C & CXDI-810C)Note 1: DQE Performance is similar. When operated under the intended use, it did not raise new safety and effectiveness concerns.
Disk Size500GB≥500GBNote 2: The disk size of the proposed device is a range value, which is only a descriptive update, however the disk size can satisfy its intended use. So it did not raise new safety and effectiveness concerns.

Acceptance is generally implied if the new device's specifications are "Same" or the differences are justified as not raising new safety/effectiveness concerns (as indicated by "Note 1" and "Note 2").

2. Sample size used for the test set and the data provenance:

  • Test Set Sample Size: The document states: "Sample image of Head, chest, abdomen, spine, pelvis, upper extremity and lower extremity were provided with a board certified radiologist to evaluate the image quality in this submission." It does not specify the exact number of images or cases in this sample set. It's described as "Sample image," implying a representative, but not quantitatively defined, set.
  • Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. Given that it's a 510(k) submission for a Chinese manufacturer (Shanghai United Imaging Healthcare Co.,Ltd.), the data could be from China, but this is not confirmed.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

  • Number of Experts: "a board certified radiologist" - This indicates one expert.
  • Qualifications: "board certified radiologist"

4. Adjudication method for the test set:

  • Adjudication Method: "Each image was reviewed with a statement indicating that image quality are sufficient for clinical diagnosis." This suggests a single-reader review without an explicit adjudication process involving multiple readers. It does not mention a 2+1, 3+1, or similar multi-reader adjudication.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, if so, what was the effect size of how much human readers improve with AI vs without AI assistance:

  • No MRMC Study: The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study. There is no mention of AI assistance or human readers improving with AI vs. without AI. The device is a mobile X-ray system, and the changes relate to its hardware (detectors) and basic control software, not an AI-powered diagnostic tool requiring such a study for a 510(k).

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

  • Not Applicable in the traditional sense: This device is an X-ray imaging system, not an AI algorithm for diagnosis. The "performance data" provided ("Clinical Image Evaluation") is about the quality of the images produced, which are then interpreted by a human. It's not a standalone diagnostic algorithm.

7. The type of ground truth used:

  • Expert Consensus (single expert, effectively): The "ground truth" for image quality assessment was established by a single "board certified radiologist" who determined if the "image quality [is] sufficient for clinical diagnosis." This is effectively expert opinion/assessment rather than a gold standard like pathology or long-term outcomes data.

8. The sample size for the training set:

  • The document does not specify a sample size for a training set. This is generally because the submission is for a conventional imaging device with new detectors, rather than an AI/Machine Learning device that requires explicit training data and validation sets. The "software" mentioned (CXDI Control Software NE) is for detector control and image acquisition/processing, not a deep learning algorithm.

9. How the ground truth for the training set was established:

  • Not applicable/Not provided: As no training set is mentioned in the context of AI/ML, there is no discussion of how ground truth for such a set was established. The "Clinical Image Evaluation" section focuses on verification of image quality for the new detectors.

§ 892.1720 Mobile x-ray system.

(a)
Identification. A mobile x-ray system is a transportable device system intended to be used to generate and control x-ray for diagnostic procedures. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.