(269 days)
Not Found
Yes
The device description explicitly states that the motion data is "analyzed using machine learning models" and that the algorithm was "developed using machine learning."
No.
The device is intended to quantify movement disorder symptoms, not to treat them.
Yes
The intended use of NeuroRPM explicitly states its purpose is to "quantify movement disorder symptoms", which includes generating "binary symptom classifications". This process of classifying symptoms directly contributes to diagnosis and monitoring of a medical condition.
No
While the device is a software application, it relies on the Apple Watch hardware to collect data, and the 510(k) summary describes the device as a "software application for the Apple Watch," implying a dependency on the hardware for its function.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- NeuroRPM's Function: NeuroRPM quantifies movement disorder symptoms by collecting accelerometer and gyroscope data from a wearable device (Apple Watch). This data is collected externally from the body and does not involve the analysis of biological samples.
Therefore, NeuroRPM falls outside the scope of an In Vitro Diagnostic device. It is a software application that uses external sensor data to assess a patient's condition.
No
The provided document does not contain any explicit statement that the FDA has reviewed, approved, or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
NeuroRPM is intended to quantify movement disorder symptoms during wake periods in adult patients 46 to 85 years of age with Parkinson's disease. These symptoms include tremor, bradykinesia, and dyskinesia. NeuroRPM is intended for clinic and home environments.
Product codes (comma separated list FDA assigned to the subject device)
GYD, ISD
Device Description
NeuroRPM is a software application for the Apple Watch that is prescribed by a health professional to quantify motor symptoms of Parkinson's disease including bradykinesia, dyskinesia, and tremor. NeuroRPM collects accelerometer and gyroscope data from the Apple Watch. The motion data are transmitted to cloud servers and analyzed using machine learning models developed to generate binary symptom classifications. Binary symptom classification output is generated every 15-minutes. A description of the NeuroRPM outputs is provided in Table 1.
Mentions image processing
Not Found
Mentions AI, DNN, or ML
...analyzed using machine learning models...
...the proposed algorithm was developed using machine learning.
Input Imaging Modality
Not Found
Anatomical Site
Not Found
Indicated Patient Age Range
46 to 85 years of age
Intended User / Care Setting
clinic and home environments.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
An observational, non-intervention study in 36 subjects was conducted to evaluate NeuroRPM's ability to quantify Parkinson's symptom presence or absence. The primary endpoints for demonstrating the performance of the NeuroRPM outputs were sensitivity and specificity. Subjects who were previously diagnosed with Parkinson's disease were enrolled in the study. Scores for each subject were obtained clinical scales, the Unified Parkinson Disease Rating Scale (UPDRS) and the Abnormal Involuntary Movement Scale (AIMS). The ground truth for each sample was derived based on the majority score of an expert rater panel of 3 board-certified movement disorder specialists.
The summary of subject demographics is provided below and is from a single site with 95.5% Caucasian subjects.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Performance Bench Testing:
Bench testing was conducted to verify that the motion data from the Apple Watch reflected the subject's activities within the expected signal patterns and range of values, and that there were no outliers. Raw motion data from test subjects wearing Apple Watch were collected during UPDRS and AIMS evaluation and analyzed in time and frequency domains. The test results demonstrated that the motion data for all subjects were consistent and reliable.
Performance Clinical Testing:
An observational, non-intervention study in 36 subjects was conducted to evaluate NeuroRPM's ability to quantify Parkinson's symptom presence or absence. The primary endpoints for demonstrating the performance of the NeuroRPM outputs were sensitivity and specificity. Subjects who were previously diagnosed with Parkinson's disease were enrolled in the study. Scores for each subject were obtained clinical scales, the Unified Parkinson Disease Rating Scale (UPDRS) and the Abnormal Involuntary Movement Scale (AIMS). The ground truth for each sample was derived based on the majority score of an expert rater panel of 3 board-certified movement disorder specialists.
The validation study was not specifically tested in the home environment; however, additional supportive data from the clinic, where subjects performed common at-home tasks and observed naturalistic behaviors, were provided to demonstrate the potential for similar performance in the home environment.
Clinical performance testing demonstrated that NeuroRPM is substantially equivalent to the predicate device. 95% confidence intervals were estimated based on the subject cluster bootstrap method.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
| NeuroRPM Output | Sensitivity [95% CI] | Specificity [95% CI] |
|---|---|---|
| Tremor | 0.7176 [0.6081, 0.8172] | 0.9508 [0.9119, 0.9802] |
| Bradykinesia | 0.7143 [0.5894, 0.8332] | 0.7740 [0.6787, 0.8597] |
| Dyskinesia | 0.7123 [0.5323, 0.8652] | 0.9466 [0.9069, 0.9741] |
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 882.1950 Tremor transducer.
(a)
Identification. A tremor transducer is a device used to measure the degree of tremor caused by certain diseases.(b)
Classification. Class II (performance standards).
0
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March 17, 2023
New Touch Digital Inc. Chan Lee Chief Operating Officer 3124 Dumbarton Street NW Washington, District of Columbia 20007
Re: K221772
Trade/Device Name: NeuroRPM Regulation Number: 21 CFR 882.1950 Regulation Name: Tremor Transducer Regulatory Class: Class II Product Code: GYD, ISD Dated: February 15, 2023 Received: February 15, 2023
Dear Chan Lee:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal
1
statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Patrick Antkowiak -S
for
Jay Gupta Assistant Director DHT5A: Division of Neurosurgical, Neurointerventional and Neurodiagnostic Devices OHT5: Office of Neurological and Physical Medicine Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K221772
Device Name NeuroRPM
Indications for Use (Describe)
NeuroRPM is intended to quantify movement disorder symptoms during wake periods in adult patients 46 to 85 years of age with Parkinson's disease. These symptoms include tremor, bradykinesia. NeuroRPM is intended for clinic and home environments.
| Type of Use (Select one or both, as applicable) | |
|---|---|
| Prescription Use (Part 21 CFR 801 Subpart D) | Over-The-Counter Use (21 CFR 801 Subpart C) |
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3
K221772 Traditional 510(k) SUMMARY New Touch Digital Inc.'s NeuroRPM
Submitter:
New Touch Digital Inc. 3124 Dumbarton Street NW Washington, DC 20007
Contact Person: Chan Lee Phone: (703)201-9548 Email: chan.lee@newtouchdigital.com
March 17, 2023 Date Prepared: Name of Device: NeuroRPM K221772 Common or Usual Name: NeuroRPM Classification Name: Tremor Transducer 21 CFR 882.1950 Regulatory Class: Class II Primary Product Code: GYD Secondary Product Code: ISD
Predicate Device:
| Manufacturer: | GKC Manufacturing Pty Ltd. |
|---|---|
| Trade/Device Name: | Personal Kinetigraph (PKG) System Model GKC-2000 |
| 510(K) Number: | K161717 |
| Decision Date: | September 20, 2016 |
Device Description:
NeuroRPM is a software application for the Apple Watch that is prescribed by a health professional to quantify motor symptoms of Parkinson's disease including bradykinesia, dyskinesia, and tremor. NeuroRPM collects accelerometer and gyroscope data from the Apple Watch. The motion data are transmitted to cloud servers and analyzed using machine learning models developed to generate binary symptom classifications. Binary symptom classification output is generated every 15-minutes. A description of the NeuroRPM outputs is provided in Table 1.
4
K221772
| Symptom | Output Classification | Output Description | Validated Scale |
|---|---|---|---|
| Tremor | NTD-TR A - No Tremor | No tremor detected. | UPDRS-III TR Score of 0 |
| NTD-TR B - Tremor | Tremor detected. | UPDRS-III TR Score of 1 and | |
| greater | |||
| Bradykinesia | NTD-BK A - Normal to Low | No, minor or mild bradykinesia | |
| detected. | Combined UPDRS-III | ||
| BK Score of 0, 1, 2, 3 | |||
| NTD-BK B - Medium to High | Moderate or greater than | ||
| moderate bradykinesia detected. | Combined UPDRS-III | ||
| BK Score of 4 and greater | |||
| Dyskinesia | NTD-DK A - No Dyskinesia | No dyskinesia detected. | Total AIMS Score of 0, 1 |
| NTD-DK B - Dyskinesia | Dyskinesia detected. | Total AIMS Score of 2 and | |
| greater |
Table 1 - Description of NeuroRPM Outputs
Intended Use:
NeuroRPM is intended to quantify movement disorder symptoms during wake periods in adult patients 46 to 85 years of age with Parkinson's disease. These symptoms include tremor, bradykinesia, and dyskinesia. NeuroRPM is intended for clinic and home environments.
Summary of Technological Characteristics:
The proposed device and the predicate device have similar technological characteristics. Both devices obtain a patient's movement data from a wrist-worn device. Sensor data from both the proposed and predicate wrist-worn devices are transferred to a cloud server and analyzed using algorithms to quantify the movement disorder symptoms. The predicate algorithm uses equations and thresholds, while the proposed algorithm was developed using machine learning. Although the algorithms are different, both devices provide similar quantification of the presence or absence of motor symptoms. In addition, the outputs of both devices indicate the presence of movement disorder symptoms (i.e., bradykinesia, tremor, and dyskinesia) in adult patients diagnosed with Parkinson's disease. The predicate device provides symptom scores every 2 minutes in median and percentiles which are compared to a control group with subjects with no Parkinson's disease. In comparison, NeuroRPM directly outputs the presence of 3 symptom types according to Table 1, without comparison to a control group. Although the output scales and references differ, NeuroRPM outputs were validated in a clinical trial, demonstrating that the device performance is adequate to support the intended use of quantifying movement disorder symptoms. Thus, these differences in technological characteristics do not raise different questions of safety and effectiveness.
5
K221772
Performance Bench Testing:
Bench testing was conducted to verify that the motion data from the Apple Watch reflected the subject's activities within the expected signal patterns and range of values, and that there were no outliers. Raw motion data from test subjects wearing Apple Watch were collected during UPDRS and AIMS evaluation and analyzed in time and frequency domains. The test results demonstrated that the motion data for all subjects were consistent and reliable.
Performance Clinical Testing:
An observational, non-intervention study in 36 subjects was conducted to evaluate NeuroRPM's ability to quantify Parkinson's symptom presence or absence. The primary endpoints for demonstrating the performance of the NeuroRPM outputs were sensitivity and specificity. Subjects who were previously diagnosed with Parkinson's disease were enrolled in the study. Scores for each subject were obtained clinical scales, the Unified Parkinson Disease Rating Scale (UPDRS) and the Abnormal Involuntary Movement Scale (AIMS). The ground truth for each sample was derived based on the majority score of an expert rater panel of 3 board-certified movement disorder specialists.
The summary of subject demographics is provided below and is from a single site with 95.5% Caucasian subjects.
| Demographics | Min | Mean | Max |
|---|---|---|---|
| Age | 46 | 67.7 | 85 |
| Approx. Age at Diagnosis | 35.1 | 59.8 | 81.3 |
| Years Since Diagnosis | 0.3 | 7.9 | 19 |
| Average Total UPDRS-III | |||
| Score | 3.4 | 11.1 | 26.7 |
| Number of Males | - | 18 | - |
| Number of Females | - | 18 | - |
Table 2 - Summary of Subject Demographics
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Image /page/6/Figure/0 description: The image shows the alphanumeric string "K221772" in a bold, sans-serif font. The characters are arranged horizontally, with the letter "K" followed by the numbers "221772". The text is black against a white background, creating a high contrast and making it easily readable. The overall impression is clean and straightforward.
Image /page/6/Figure/1 description: This bar graph shows the number of subjects at each Hoehn and Yahr Stage. The x-axis shows the Hoehn and Yahr Stage from 0 to 5, and the y-axis shows the number of subjects from 0 to 10. The graph shows that the most subjects are at stage 1.5, with 10 subjects.
Figure 1 - Histogram of Hoehn and Yahr Stages (n=36)
The validation study was not specifically tested in the home environment; however, additional supportive data from the clinic, where subjects performed common at-home tasks and observed naturalistic behaviors, were provided to demonstrate the potential for similar performance in the home environment.
Clinical performance testing demonstrated that NeuroRPM is substantially equivalent to the predicate device. 95% confidence intervals were estimated based on the subject cluster bootstrap method.
| NeuroRPM Output | Sensitivity [95% CI] | Specificity [95% CI] |
|---|---|---|
| Tremor | 0.7176 [0.6081, 0.8172] | 0.9508 [0.9119, 0.9802] |
| Bradykinesia | 0.7143 [0.5894, 0.8332] | 0.7740 [0.6787, 0.8597] |
| Dyskinesia | 0.7123 [0.5323, 0.8652] | 0.9466 [0.9069, 0.9741] |
Table 3 - NeuroRPM Output Sensitivity and Specificity of the event with 95% Confidence Intervals
Sample size n = 36
The analysis is based on events instead of subjects. There are a total of 36 subjects who may have all the 3 types of events. The total number of events (from truth) for sensitivity evaluation is 170 for Tremor, and 203 for BK and 73 for DK. The number of events (from truth) for specificity evaluation is 325 for Tremor, 292 for BK, and 422 for DK.
Conclusions:
NeuroRPM is substantially equivalent to GKC's Personal Kinetigraph (PKG) System Model GKC-2000. NeuroRPM has the same intended use, technological characteristics, and principles of operation as the predicate device. The differences in indications for use do not introduce a new intended use. In addition, technological differences between NeuroRPM and the predicate device do not raise different questions of safety or effectiveness.
7
K221772
Substantial equivalence comparison of the predicate device and NeuroRPM is provided in Table 2.
| | Predicate Device:
GKC PKG System
(K161717) | Subject Device:
NeuroRPM | Comparison |
|------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Indications for
Use | The Personal Kinetigraph
(PKG) is intended to quantify
kinematics of movement
disorder symptoms in
conditions such as
Parkinson's disease,
including tremor,
bradykinesia and dyskinesia.
It includes a medication
reminder, an event marker
and is intended to monitor
activity associated with
movement during sleep. The
device is indicated for use in
individuals 46 to 83 years of
age. | NeuroRPM is intended to
quantify movement disorder
symptoms during wake
periods in adult patients 46
to 85 years of age with
Parkinson's disease. These
symptoms include tremor,
bradykinesia, and
dyskinesia. NeuroRPM is
intended for clinic and
home environments. | Same intended use of
quantifying movement
disorder symptoms. |
| Wearable
Device | Proprietary watch with
accelerometer. | Apple Watch triaxial inertial
measurement unit. | Both use wrist-worn devices
to measure movement. |
| Algorithm | Equation with data in
frequency domain. | Machine learning model with
data in time and frequency
domain. | Both algorithms are
deterministic and generate
classifications. |
| Symptom
Measured | Tremor, bradykinesia and
dyskinesia. | Tremor, bradykinesia, and
dyskinesia. | Same symptom
measurements. The subject
device produces binary
symptom outputs. |
| Outputs | Symptom measurement
every 2 minutes in median
and percentiles which are
compared to a control group
with subjects with no
Parkison's disease. | Symptom presence or
absence levels every 15-
minutes. | NeuroRPM provides direct
symptom measurement
without comparison to a
control group. 15-minute
intervals provide sufficient
characterization of whether a
symptom is present. |
| Output Report | PDF report with daily graphs
and summary statistics of
symptom outputs. | PDF report with daily graphs
and summary statistics of
symptom outputs. | Same output report formats
with similar symptom
information and
symptom outputs. |
No
Medication
Reminder
Yes
Table 2 - Substantial Equivalence Comparison of Predicate Device and NeuroRPM
visualizations.
Not required for
disorder symptoms.
quantification of movement