Last synced on 25 November 2022 at 11:04 pm

Diabetic Retinopathy Detection Device

Page Type
Product Code
Definition
A retinal diagnostic software device is a prescription software device that incorporates an adaptive algorithm to evaluate ophthalmic images for diagnostic screening to identify retinal diseases or conditions.
Physical State
Installed on, and operated by, an off-the-shelf hardware system with an appropriate OS.
Technical Method
Software as a medical device designed to analyze digital fundus images taken with an ophthalmic camera. Using artificial intelligence algorithms, the device is able to determine whether a patient has referable retinopathy.
Target Area
Fundus images of the macula and optic nerve of the eye.
Regulation Medical Specialty
Ophthalmic
Review Panel
Ophthalmic
Submission Type
510(K)
Device Classification
Class 2
Regulation Number
886.1100
GMP Exempt?
No
Summary Malfunction Reporting
Eligible
Implanted Device
No
Life-Sustain/Support Device
No
Third Party Review
Not Third Party Eligible

CFR § 886.1100 Retinal diagnostic software device

§ 886.1100 Retinal diagnostic software device.

(a) Identification. A retinal diagnostic software device is a prescription software device that incorporates an adaptive algorithm to evaluate ophthalmic images for diagnostic screening to identify retinal diseases or conditions.

(b) Classification. Class II (special controls). The special controls for this device are:

(1) Software verification and validation documentation, based on a comprehensive hazard analysis, must fulfill the following:

(i) Software documentation must provide a full characterization of technical parameters of the software, including algorithm(s).

(ii) Software documentation must describe the expected impact of applicable image acquisition hardware characteristics on performance and associated minimum specifications.

(iii) Software documentation must include a cybersecurity vulnerability and management process to assure software functionality.

(iv) Software documentation must include mitigation measures to manage failure of any subsystem components with respect to incorrect patient reports and operator failures.

(2) Clinical performance data supporting the indications for use must be provided, including the following:

(i) Clinical performance testing must evaluate sensitivity, specificity, positive predictive value, and negative predictive value for each endpoint reported for the indicated disease or condition across the range of available device outcomes.

(ii) Clinical performance testing must evaluate performance under anticipated conditions of use.

(iii) Statistical methods must include the following:

(A) Where multiple samples from the same patient are used, statistical analysis must not assume statistical independence without adequate justification.

(B) Statistical analysis must provide confidence intervals for each performance metric.

(iv) Clinical data must evaluate the variability in output performance due to both the user and the image acquisition device used.

(3) A training program with instructions on how to acquire and process quality images must be provided.

(4) Human factors validation testing that evaluates the effect of the training program on user performance must be provided.

(5) A protocol must be developed that describes the level of change in device technical specifications that could significantly affect the safety or effectiveness of the device.

(6) Labeling must include:

(i) Instructions for use, including a description of how to obtain quality images and how device performance is affected by user interaction and user training;

(ii) The type of imaging data used, what the device outputs to the user, and whether the output is qualitative or quantitative;

(iii) Warnings regarding image acquisition factors that affect image quality;

(iv) Warnings regarding interpretation of the provided outcomes, including:

(A) A warning that the device is not to be used to screen for the presence of diseases or conditions beyond its indicated uses;

(B) A warning that the device provides a screening diagnosis only and that it is critical that the patient be advised to receive followup care; and

(C) A warning that the device does not treat the screened disease;

(v) A summary of the clinical performance of the device for each output, with confidence intervals; and

(vi) A summary of the clinical performance testing conducted with the device, including a description of the patient population and clinical environment under which it was evaluated.

[87 FR 3205, Jan. 21, 2022]

Diabetic Retinopathy Detection Device

Page Type
Product Code
Definition
A retinal diagnostic software device is a prescription software device that incorporates an adaptive algorithm to evaluate ophthalmic images for diagnostic screening to identify retinal diseases or conditions.
Physical State
Installed on, and operated by, an off-the-shelf hardware system with an appropriate OS.
Technical Method
Software as a medical device designed to analyze digital fundus images taken with an ophthalmic camera. Using artificial intelligence algorithms, the device is able to determine whether a patient has referable retinopathy.
Target Area
Fundus images of the macula and optic nerve of the eye.
Regulation Medical Specialty
Ophthalmic
Review Panel
Ophthalmic
Submission Type
510(K)
Device Classification
Class 2
Regulation Number
886.1100
GMP Exempt?
No
Summary Malfunction Reporting
Eligible
Implanted Device
No
Life-Sustain/Support Device
No
Third Party Review
Not Third Party Eligible

CFR § 886.1100 Retinal diagnostic software device

§ 886.1100 Retinal diagnostic software device.

(a) Identification. A retinal diagnostic software device is a prescription software device that incorporates an adaptive algorithm to evaluate ophthalmic images for diagnostic screening to identify retinal diseases or conditions.

(b) Classification. Class II (special controls). The special controls for this device are:

(1) Software verification and validation documentation, based on a comprehensive hazard analysis, must fulfill the following:

(i) Software documentation must provide a full characterization of technical parameters of the software, including algorithm(s).

(ii) Software documentation must describe the expected impact of applicable image acquisition hardware characteristics on performance and associated minimum specifications.

(iii) Software documentation must include a cybersecurity vulnerability and management process to assure software functionality.

(iv) Software documentation must include mitigation measures to manage failure of any subsystem components with respect to incorrect patient reports and operator failures.

(2) Clinical performance data supporting the indications for use must be provided, including the following:

(i) Clinical performance testing must evaluate sensitivity, specificity, positive predictive value, and negative predictive value for each endpoint reported for the indicated disease or condition across the range of available device outcomes.

(ii) Clinical performance testing must evaluate performance under anticipated conditions of use.

(iii) Statistical methods must include the following:

(A) Where multiple samples from the same patient are used, statistical analysis must not assume statistical independence without adequate justification.

(B) Statistical analysis must provide confidence intervals for each performance metric.

(iv) Clinical data must evaluate the variability in output performance due to both the user and the image acquisition device used.

(3) A training program with instructions on how to acquire and process quality images must be provided.

(4) Human factors validation testing that evaluates the effect of the training program on user performance must be provided.

(5) A protocol must be developed that describes the level of change in device technical specifications that could significantly affect the safety or effectiveness of the device.

(6) Labeling must include:

(i) Instructions for use, including a description of how to obtain quality images and how device performance is affected by user interaction and user training;

(ii) The type of imaging data used, what the device outputs to the user, and whether the output is qualitative or quantitative;

(iii) Warnings regarding image acquisition factors that affect image quality;

(iv) Warnings regarding interpretation of the provided outcomes, including:

(A) A warning that the device is not to be used to screen for the presence of diseases or conditions beyond its indicated uses;

(B) A warning that the device provides a screening diagnosis only and that it is critical that the patient be advised to receive followup care; and

(C) A warning that the device does not treat the screened disease;

(v) A summary of the clinical performance of the device for each output, with confidence intervals; and

(vi) A summary of the clinical performance testing conducted with the device, including a description of the patient population and clinical environment under which it was evaluated.

[87 FR 3205, Jan. 21, 2022]