K Number
K243900
Manufacturer
Date Cleared
2025-06-27

(190 days)

Product Code
Regulation Number
892.5050
Panel
RA
Reference & Predicate Devices
N/A
AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
Intended Use

The EmpNia eMotus system is used to measure and record the patient's respiratory waveform to aid with respiratory-synchronized image acquisition or reconstruction during CT diagnostic imaging or radiation treatment planning procedures, where there is a risk of respiratory motion compromising the resulting image.

The EmpNia eMotus system is used to derive and communicate a Gate signal to aid with organ position verification for radiation therapy treatment using CT or Xray imaging by monitoring the patient's respiratory waveform during the image acquisition, where there is a risk of respiratory motion compromising the resulting image.

The EmpNia eMotus system is used to derive and communicate a Gate signal to aid with radiation therapy treatment, where there is a risk of respiratory motion compromising the resulting treatment accuracy.

Device Description

The eMotus Respiratory Motion Management System ("eMotus system") is designed to monitor patient respiratory motion and to provide information about this respiratory motion to an external medical device system, such as a radiation therapy delivery device (TDD) or a diagnostic imaging device (DX). The main components of the eMotus system include:

  • Sensor pad with optical fiber sensors,
  • Optical fiber cables,
  • Optical transceiver,
  • Data acquisition computer with eMotus software application,
  • Communication modules for compatible external systems, and
  • Cables to allow data transmission between the components.

The sensor pad is a single-use, disposable component with an adhesive backing that is placed directly on the patient's thorax or abdomen. The sensor pad is attached to optical fiber cables that connect to the optical transceiver, which collects optical signal data based on deflection of the sensors in response to respiratory motion. The transceiver digitizes the data and transmits it to the eMotus computer, which visualizes the data as a waveform that can be highlighted when the waveform amplitude reaches a user-specified threshold or the patient's respiratory cycle reaches a user-specified phase. The user can utilize the respiratory threshold and phase information to manually control an external TDD or DX system.

When connected to an external TDD or DX, the eMotus system supports the following functions (as applicable given the functions of the external system):

  • Threshold-gated therapy delivery: Automatic gating (turning on / off) of the radiation treatment beam based on user-set parameters for the amplitude of the respiratory waveform.
  • Phase-gated therapy delivery: Automatic gating (turning on / off) of the radiation treatment beam based on user-set parameters for the phase of the respiratory waveform cycle.
  • Retrospective four-dimensional planning scan: Delivery of the respiratory waveform to an imaging device to synchronize the waveform data with the scan data, enabling retrospective four-dimensional reconstruction of the imaging session for use in treatment planning.
  • Prospective four-dimensional planning scan: Automatic patient's respiratory waveform are within preset limits, which is used to disable the radiation beam automatically.

The eMotus device is an ancillary device and does not provide stand-alone therapy or diagnostic information.

AI/ML Overview

Unfortunately, the provided text does not contain the detailed study information required to answer many of your questions. The 510(k) summary focuses on demonstrating "substantial equivalence" to a predicate device, and while it mentions "bench performance," it lacks the specific methodology, sample sizes, and expert involvement that would typically be present in a comprehensive clinical or standalone performance study report.

Here's a breakdown of what can and cannot be answered based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document mentions "Bench performance" testing but does not explicitly state formal acceptance criteria in a quantitative sense, nor does it provide specific numerical performance metrics beyond "nearly identical signals" and "stable dynamics."

Acceptance Criteria (Inferred from "Bench Performance")Reported Device Performance (from text)
Generation of equivalent respiratory waveforms compared to predicate deviceComparative evaluations showed that the subject and predicate devices produce equivalent respiratory waveforms.
Signal latency $\leq 50$msSupported that the subject device meets its requirement for signal latency.
Stable dynamics and peak frequency in infant and adult phantoms at normal and fast breathing frequenciesHas stable dynamics and peak frequency in infant and adult phantoms at normal and fast breathing frequencies.
Correctly pauses gating, sets the gate to off, and alerts the user when there is irregular breathingCorrectly pauses gating, sets the gate to off, and alerts the user when there is irregular breathing.
Consistent, repeatable, and reproducible behavior over multiple sensorsShows consistent, repeatable, and reproducible behavior over multiple sensors.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

  • Sample Size for Test Set: Not specified. The text mentions "infant and adult phantoms" and "multiple sensors" but does not give specific numbers.
  • Data Provenance: The study described as "bench performance" clearly implies a laboratory/simulated environment rather than clinical data from human patients. Therefore, information about country of origin, retrospective or prospective data, is not applicable or provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not specified. Given it was "bench performance" with phantoms and a comparison to a predicate device, it's unlikely human experts were establishing ground truth in the traditional sense. The "ground truth" was likely derived from the known simulated respiratory patterns and the output of the predicate device.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable/Not specified. Adjudication methods are typically used when human reviewers are involved in assessing complex outputs. This was a bench performance study comparing waveforms and functionality.

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, an MRMC comparative effectiveness study was not explicitly done or described. The device is a "Respiratory Motion Management System," which aids in synchronizing image acquisition or radiation treatment – it's not an AI diagnostic tool that human readers would directly interpret to improve diagnostic accuracy in the way an MRMC study typically assesses. Therefore, the effect size for human reader improvement is not applicable to the information provided.

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

Yes, the "bench performance" described primarily represents a standalone evaluation of the eMotus system's technical capabilities in a controlled environment, comparing its output directly to known inputs and the predicate device's output. The "human factors" testing mentioned separately focuses on usability, but the core performance data is standalone.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

The "ground truth" for the bench performance testing appears to be based on:

  • Known simulated respiratory patterns (for assessing stable dynamics, peak frequency, irregular breathing alerts).
  • Output of the predicate device (for comparing respiratory waveforms).

8. The sample size for the training set

The document does not mention any training set size, which suggests that the device, being a physiological signal monitoring and gating system, likely does not involve machine learning or AI that requires a labeled training set in the conventional sense for its core functionality. Its "software functions" are verified and validated, indicating traditional software engineering practices.

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

Not applicable/Not specified, as no training set is mentioned in the provided text.

In summary, the provided FDA 510(k) clearance letter and summary are designed to demonstrate substantial equivalence, not to provide a detailed clinical or standalone performance study report with the specific metrics you've requested beyond what's inferable from the "bench performance" section.

§ 892.5050 Medical charged-particle radiation therapy system.

(a)
Identification. A medical charged-particle radiation therapy system is a device that produces by acceleration high energy charged particles (e.g., electrons and protons) intended for use in radiation therapy. This generic type of device may include signal analysis and display equipment, patient and equipment supports, treatment planning computer programs, component parts, and accessories.(b)
Classification. Class II. When intended for use as a quality control system, the film dosimetry system (film scanning system) included as an accessory to the device described in paragraph (a) of this section, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.