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The Resolve Fundus Camera is an automatic eye-fundus camera intended for taking digital images of a human retina with or without the use of a mydriatic agent. It is intended for use as an aid to clinicians in the evaluation and diagnosis of ocular health.

The Resolve Fundus Camera is a fundus camera designed to perform fundus observation, automatic pupil tracking and focusing, automatic image capture, and image preservation. The Resolve Fundus Camera is used for non-mydriatic observation and capturing of retinal images. The fundus camera employes three internal imaging systems to operate: illumination system, imaging system and observation system. Auto-alignment and auto-focus algorithms are used to automatically find and capture desired images of the fundus.

The fundus camera features an internal, movable three-dimensional platform that allows for switching and precise positioning of the left and right eyes. The imaging process is done one eye at a time (left eye and then the right eye). The internal, moveable three-dimensional platform contains two types of cameras: a set of infrared cameras (one on each side) as well as one main camera.

The observation system is used to detect and track the patient's pupil. Next, the illumination system determines the precise location of the ocular fundus after the pupil is located by the observation system. The system precisely finds the relationship between the position of the ocular fundus at different diopters, and the lens of the main camera.

Once the location of the fundus is found, a charge-coupled device (CCD) automatically captures a still image of the fundus through the main camera. The autofocus system utilizes a beam splitter to split a beam of light into two fine beams, which then image the ocular fundus onto the CCD. When the focus is at its sharpest position, the split beams align horizontally. At this point, the LED white light illumination system, installed in the imaging module on the three-dimensional movable platform, emits uniform white light of appropriate intensity to illuminate the ocular fundus. The imaging system captures the fundus information onto the CCD, and the received signals are displayed in real-time on a liquid crystal display (LCD) through the control system. Doctors can visually assess the patient's ocular fundus on the LCD screen.

The provided FDA 510(k) clearance letter for the Optain Health Resolve Fundus Camera focuses primarily on establishing substantial equivalence to a predicate device (Next Sight Srl Nexy). For this type of device (an ophthalmic camera), the clearance process in this document does not necessitate a detailed clinical study for performance evaluation that would typically involve acceptance criteria for diagnostic accuracy (e.g., sensitivity, specificity) of an AI algorithm. Instead, the performance data presented is focused on demonstrating that the device meets safety and basic functional standards, similar to the predicate device.

Therefore, many of the requested points regarding AI algorithm performance (like specific acceptance criteria for diagnostic accuracy, sample sizes for test sets, expert adjudication methods, MRMC studies, standalone performance, and ground truth establishment for training/test sets) are not explicitly described or required for this particular regulatory submission type, given the device's classification and stated indications for use. The device's indication is for "taking digital images" and "aid to clinicians in the evaluation and diagnosis of ocular health," implying that the device primarily performs an imaging function, and any "diagnosis" is still ultimately made by the human clinician using the image.

Below is an attempt to answer the questions based only on the provided text. Where information is not available, it will be stated as such.

Device: Optain Health Resolve Fundus Camera

Indications for Use: The Resolve Fundus Camera is an automatic eye-fundus camera intended for taking digital images of a human retina with or without the use of a mydriatic agent. It is intended for use as an aid to clinicians in the evaluation and diagnosis of ocular health.

Acceptance Criteria and Reported Device Performance

The document describes the device's performance in terms of compliance with recognized consensus standards rather than diagnostic performance metrics (e.g., sensitivity, specificity, accuracy) for an AI algorithm. The acceptance criteria are implicit in meeting the requirements of these standards.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• The document states "Non-clinical testing was performed," and "Performance testing" but does not specify a "test set" in the context of a dataset for evaluating an AI algorithm's diagnostic performance.
• The tests are primarily related to general device safety, function, and image quality standards compliance, not the diagnostic accuracy of an AI.
• Data Provenance: Not specified, but likely laboratory or engineering test data, not patient data from a specific country or retrospective/prospective study.

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

• Not applicable/Not specified in the provided document, as no specific diagnostic ground truth for an AI algorithm's performance is described. The device's function is to aid clinicians, not to output a diagnosis via AI.

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

• Not applicable/Not specified, for the same reasons as above.

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 is described in this 510(k) summary. The device's clearance is based on substantial equivalence to a predicate ophthalmic camera, not on a claim of AI-assisted diagnostic improvement.

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

• No standalone AI performance study is described. The device is an "automatic eye-fundus camera" which "aids clinicians." There is no mention of an AI algorithm producing a diagnostic output independently of a human.

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

• Not applicable/Not specified, as the performance evaluation is focused on device functionality and safety standards, not diagnostic accuracy against a clinical ground truth.

8. The sample size for the training set:

• Not applicable/Not specified. The document does not describe a training set for an AI algorithm. If there are "auto-alignment and auto-focus algorithms," these might involve machine learning, but the document does not detail their training data or performance evaluation beyond general "software verification and validation."

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

• Not applicable/Not specified, for the same reasons as above.

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HFC-1 fundus camera is intended to capture digital images for the anterior and retina segment of the eye without the use of a mydriatic agent. It is intended for use as an aid to clinicians in the evaluation and diagnosis of ocular health.

HFC-1 Fundus Camera captures, store and display color fundus images with built-in 20 Mega pixel colored channel up to 45-degree field of view. HFC-1 Fundus Camera is designed as a non-contact, non-invasive and high resolution digital imaging device. HFC-1 Fundus Camera has a retinal imaging system that provides digital images of the eyes to assist physicians in diagnostic examinations. The anterior of an eye is illuminated by IR light, the retina of an eye is illuminated by a white LED, emitted by the fundus illumination optical system. The fundus observation/photography optical system obtains an image with image sensors and images are observed and manipulated on the display panel.

The Huvitz Co., Ltd. HFC-1 Fundus Camera is intended to capture digital images for the anterior and retina segment of the eye without the use of a mydriatic agent, aiding clinicians in evaluating and diagnosing ocular health.

The device's performance was evaluated through a series of bench tests, including electrical and mechanical safety testing, electromagnetic compatibility, light hazard testing, and disinfection tests, all adhering to relevant international standards. The primary effectiveness study involved comparing the HFC-1's image quality and technical features against a predicate device, the Nidek AFC-330 Non-Mydriatic Auto Fundus Camera (K113451), and assessing its conformity to ISO 10940:2009 (Ophthalmic Instruments-Fundus Cameras).

Here's a breakdown of the acceptance criteria and study details:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: The document does not specify a distinct "test set" in terms of patient images or clinical cases for the performance evaluation. Instead, the performance tests relied on model eyes, standardized targets (e.g., USAF chart, scales), and physical measurements of the device.
• Data Provenance: The testing appears to be prospective bench testing conducted by the manufacturer, Huvitz Co., Ltd., which is based in Gyeonggi-do, Republic of Korea. No patient data or clinical data from specific countries are mentioned for these performance tests.

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)

• For the objective quantitative tests (e.g., resolution, image capture angle, pupil diameter, pixel pitch, working distance, moving range, auto tracking, sleep mode, LCD tilting angle, cornea flare, lens flare), the acceptance criteria were established based on ISO 10940 Standard or opinions of Huvitz senior engineers and researchers. There is no mention of external experts or their specific qualifications for establishing ground truth for these objective measurements.
• For the Image Quality Comparison Test, images from the HFC-1 and the predicate device were "shown to the physician for comparison in image quality." The document does not specify the number of physicians, their qualifications, or how their comparisons were aggregated to form a "ground truth" or judgment.

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

• For the objective bench tests, the adjudication method was none in the sense of expert consensus. The results were compared directly against pre-defined numerical or descriptive criteria derived from ISO standards or internal expert opinion.
• For the Image Quality Comparison Test, the document states images were "shown to the physician for comparison," but it does not describe an adjudication method (e.g., majority vote, consensus meeting) for interpreting the physician's comparison.

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 comparative effectiveness study was done. The device is a fundus camera, which is an imaging device, not an AI-powered diagnostic algorithm designed to assist human readers. The effectiveness study focused on the image capture capabilities and image quality of the camera itself.

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

• This question is not applicable as the HFC-1 Fundus Camera is an imaging device, not an AI algorithm. Its performance is about its ability to capture images, not interpret them.

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

• For the objective performance tests, the "ground truth" was based on internationally recognized standards (ISO 10940) and internal engineering specifications/expert opinions.
• For the Image Quality Comparison Test, the "ground truth" was based on the direct visual comparison of images by an unnamed physician. This is closer to a subjective expert assessment rather than objective and independently verified ground truth like pathology.

8. The sample size for the training set

• No training set is mentioned or applicable, as the HFC-1 Fundus Camera is an imaging device, not a machine learning algorithm that requires training data.

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

• Not applicable as there is no training set for this device.

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The Fundus Camera RetiCam 3100 is intended to capture digital images of the posterior and external structures of the eye without the use of a mydriatic agent and is intended for use as an aid to clinicians in the evaluation, diagnosis and documentation of ocular health.

The RetiCam 3100 fundus camera is capable to capture, display, story, manage, process digital images of the posterior and external structures of the eye continuously, in real time.

The RetiCam 3100 fundus camera displays color fundus images continuously, in real time. The fundus digital image acquisition and image processing system is capable to perform measurement of the length (digital caliper), and perform area measurements, image comparison and image montage.

The RetiCam 3100 fundus camera function module includes: optical module, mobile platform module, power supply module, control module, chin rest module.

Optical Module
The optical module is responsible for providing background light illumination, flash shooting, fixing lamp control, lens focal length adjustment and pupil monitoring of patients when the fundus camera works. The optical module mainly consists of two parts: the light source module and the lens light path module.

• 1. The light source component is the source of the light source when the product works, including background light, fixation light and flash when shooting.
• 2. The lens light path is responsible for reflecting the patient's fundus image to the digital camera. The functions of lens focusing, pupil size switching and double camera pupil monitoring are used to ensure that the patient's fundus image can be presented clearly and reliably.

Mobile Platform Module
This module is mainly responsible for control of the position of optical module (i.e. up, down, left, right, back and forth movement of the optical module). This includes X, Y, Z axes, which are designed with servo motor and bearing, which are controlled by PCB board in the control module.

Power Supply Module
Power supply to each module through medical switching power supply.

Control Module
To enable operators to control the product, understand the working status of the product, observe the patient's condition, and carry out the corresponding inspection work normally.

There is a computer system integrated in the product, the input and output function are realized by touch screen display.

Chin Rest Module
To place the patient's head and keep the patient's eyes stable and easy to observe.

The provided document is a 510(k) summary for the Fundus Camera RetiCam 3100. It focuses on demonstrating substantial equivalence to predicate devices rather than proving performance against specific acceptance criteria for an AI/algorithm-based diagnostic device.

Here's a breakdown of why many of your requested items cannot be found in this document:

• This document describes a medical device (a fundus camera) for image capture, not an AI/algorithm for diagnosis. Therefore, the concepts of "acceptance criteria for an AI algorithm," "AI vs. without AI assistance," "standalone algorithm performance," and "ground truth for test/training sets" are not applicable to the content presented.

However, I can extract information related to the performance specifications and the study (non-clinical testing) conducted for the device itself.

Acceptance Criteria and Reported Device Performance (Non-Clinical Testing)

Study Proving Device Meets Acceptance Criteria:

The study that proves the device meets the acceptance criteria is a non-clinical test conducted by Chongqing Bio NewVision Medical Equipment Ltd. This testing aimed to verify that the proposed device met all design specifications and was substantially equivalent to the predicate device.

Here's what can be inferred/extracted about the "study":

1. Sample size used for the test set and the data provenance: Not applicable/Not provided. This was not a study involving human data or labeled datasets for an algorithm. It was a performance validation of a hardware device.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable/Not provided. This was a technical performance test of a camera, not a diagnostic assessment requiring expert ground truth.
3. Adjudication method for the test set: Not applicable/Not provided.
4. 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. This document explicitly states, "No clinical study is included in this submission." Furthermore, the device is a fundus camera, not an AI algorithm, so the concept of human readers improving with AI assistance is not relevant to this submission.
5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: No. This device is a fundus camera, not a standalone algorithm.
6. The type of ground truth used: For the performance parameters (resolving power, field of view, pixel pitch, range of focus), the "ground truth" was established by technical specifications and measurement standards, specifically ISO 10940, rather than clinical outcomes or expert consensus.
7. The sample size for the training set: Not applicable/Not provided. This is a hardware device; there is no training set for an AI algorithm.
8. How the ground truth for the training set was established: Not applicable/Not provided.

Summary of Non-Clinical Testing:

The non-clinical tests involved verifying compliance with several international and national standards, including:

• AAMI/ANSI/ES 60601-1:2005+A1:2012 (Medical Electrical Equipment - Basic Safety and Essential Performance)
• IEC 60601-1-2:2014 (EMC for Medical Electrical Equipment)
• ISO 15004-1:2006 (Ophthalmic instruments - General requirements)
• ISO 15004-2:2007 (Ophthalmic instruments - Light hazard protection)
• ISO 10940:2009 (Ophthalmic instruments - Fundus cameras)
• ISO 10993-5:2009 & ISO 10993-10:2010 (Biological Evaluation of Medical Device - Cytotoxicity, Irritation, and Hypersensitivity)
• ANSI Z80.36-2016 (American National Standard for Ophthalmics - Light Hazard Protection)

The results demonstrated that the proposed device complies with these standards, specifically meeting or exceeding the resolving power, field of view, pixel pitch, and focus range specifications outlined in ISO 10940.

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NFC-700 is a non-contact, high resolution digital imaging device which is suitable for photographing, displaying and storing images of the retina and external areas of the eye to be evaluated under non-mydriatic conditions. NFC-700 is indicated for in-vivo viewing of the posterior and external area of the eye and the images are intended for use as an aid to clinicians in the evaluation, diagnosis and documentation of ocular health.

The NFC-700 is a non-contact fundus camera for capturing, storing and displaying the color fundus images with 12MP. It was designed a non-contact, high resolution digital imaging device, auto 3D tracking, fast and easy to use retinal imaging system and provide images of the eye as an aid to clinicians in the diagnosis of diabetic retinopathy, AMD, glaucoma and other retinal diseases. NFC-700 was designed as an All-in-one system with full auto-focusing technique and easy operation. The large 10.1" touch screen makes it easy to control all of the operating procedures and makes the measurement and image check easily. NFC-700 uses NIR LED as illumination during alignment to the retina of patients' eyes, users can just touch the center of the pupil on the screen to capture the image. There also some interface located at the bottom of the device such as USB, HDMI, Ethernet make user to store, retrieve, archive and share the digital images by USB or LAN.

Here's a breakdown of the acceptance criteria and the study details for the NFC-700 non-mydriatic auto fundus camera, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

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

   • Test Set Sample Size: 119 patients (eyes).
   • Data Provenance: The text does not explicitly state the country of origin. It describes a "single-site study" and mentions "Consented subjects will undergo ophthalmic examination." This suggests it was a prospective study.

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

   • Number of Experts: One ophthalmologist.
   • Qualifications: "The ophthalmologist reviews all fundus images..." (No further specific qualifications or years of experience are provided beyond "ophthalmologist").

3. Adjudication method for the test set:

   • The text describes a single ophthalmologist reviewing and grading the images. There is no mention of an adjudication method for discrepancies, implying either none was used or no discrepancies requiring adjudication were present (unlikely in a subjective grading process).

4. 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 done. This study compared the image quality of the investigational device (NFC-700) to a reference camera (FundusVue), not the performance of human readers with or without AI assistance. The NFC-700 is a camera, not an AI diagnostic tool.

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

   • Yes, in spirit. The "Performance Data" section primarily details the standalone performance of the NFC-700 camera in terms of image quality and technical specifications. The "Clinical Testing" evaluated the camera's ability to produce diagnostically useful images, which is a standalone assessment of the camera itself. It's important to note this is for the camera's image output, not an AI algorithm's diagnostic output.

6. The type of ground truth used:

   • Expert Consensus (Single Expert): The ground truth for image quality assessment was established by a single ophthalmologist's review using a 5-point grading scale and assessment against clinically significant features.

7. The sample size for the training set:

   • The document does not provide information regarding a training set. This is a fundus camera, not an AI algorithm that typically requires a large training set for model development. The mentions of "Software verification and validation testing" relate to the camera's control software, not an image analysis AI.

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

   • Since no training set information is provided, how its ground truth was established is not applicable here.

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The CSO Cobra Fundus Camera is intended for taking digital images of a human retina with or without the use of mydriatic agent. The retinal image can be stored to an image filing device.

Cobra is intended for taking digital images of a human retina with or without the use of a mydriatic agent. A function for retinal plane capturing is provided. The instrument is furnished with an integrated 5MP CCD camera, and uses one (1) white LED for flashing, one (1) IR led for alignment and Infrared acquisitioning.

Cobra can be used with pupil diameters starting from 2.5 mm, and therefore can be used without the need for a mydriatic agent. Acquisition transfer is performed from the instrument to the accompanying PC via a Firewire cable.

This document is a 510(k) premarket notification for the Cobra Fundus Camera, seeking clearance from the FDA. The information provided focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed independent clinical study with specific acceptance criteria in the typical sense for an AI/CADe device.

Therefore, many of the requested sections (e.g., effect size of human readers with AI assistance, detailed sample sizes for test and training sets with ground truth establishment for AI, number of experts for ground truth, adjudication method) are not applicable or not explicitly stated in this type of submission, as the device itself is a fundus camera, not an AI diagnostic system. The "performance test" section refers to engineering and regulatory compliance tests, not a clinical study to evaluate diagnostic accuracy against a ground truth.

Here's the closest interpretation of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: Not applicable in the context of this 510(k) submission. The "performance tests" mentioned are engineering and regulatory compliance studies, not clinical trials with patient data.
• Data Provenance: Not applicable.

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

• Not applicable. This submission doesn't describe a clinical study requiring ground truth established by experts.

4. Adjudication method for the test set

• Not applicable.

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. This device is a fundus camera, not an AI/CADe system. Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant or described.

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

• No. This is a hardware device (fundus camera), not an algorithm.

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

• Not applicable in the context of a clinical performance evaluation. The "ground truth" for the performance tests would be the fulfillment of established engineering standards (e.g., electrical safety, EMC requirements).

8. The sample size for the training set

• Not applicable. This device is a fundus camera, not an AI system that requires a "training set."

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

• Not applicable.

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The iCam takes digital images of the posterior and external structures of the eye without the use of a mydriatic agent and is intended for use as an aid to clinicians in the evaluation, diagnosis and documentation of ocular health.

The iCam is a non-contact, high resolution digital imaging device which is suitable for photographing, displaying and storing images of the retina and external areas of the eye to be evaluated under non-mydriatic conditions.

iCam is indicated for in-vivo viewing of the posterior and external area of the eye and the images are intended for use as an aid to clinicians in the evaluation, diagnosis and documentation of ocular health.

iCam provides images only and does not provide any diagnostic, pathological analysis or classification of ocular health or disease.

The iCam is a non-mydriatic fundus camera for capturing, storing and displaying color fundus images with 1.3 MP @ 12 bits per color channel up to 45 degree (axial arc) field of view. It was designed to provide an acceptable area for broad range, high resolution viewing of most retinalbased and optic nerve pathologies. The design allows for the acquisition of high quality images that are of comparable quality to other predicate ocular cameras. The design incorporates the use of an LED light sources providing two advantages over the flash lamp light source of other cameras: 1) longer life expectancy of the LED compared to the typical Xenon flash lamp, and 2) reliability of solid state devices that allow for more reproducible light characteristics over time.

The LED light source provides lower voltage operation, a higher efficiency overall and allows for smaller design of the system based on the relatively small size of the LEDs compared to the Xenon bulb. LED light source has a considerably longer lifespan than a Xenon light source, while emitting minimal heat compared to the heat generating Xenon source.

The LED light source also reliability of solid state devices that allow for more reproducible image quality over time. Solid state devices that function via an on-or-off state are known to maintain light characteristics such as color temperature, lumens of output, and distribution of light. This characteristic of no demonstrable degradation results in more reproducible images over time.

Here's a breakdown of the acceptance criteria and the study details for the Optovue iCam Fundus Camera, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The core acceptance criterion for the iCam was "substantial equivalence" to its predicate device (Centervue DRS) in terms of image quality and clinical usefulness.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: A total of 120 evaluable subjects were targeted for enrollment. For each subject, images were collected on the study eye (2 central field fundus and 1 external eye) using both the iCam and the DRS.
• Data Provenance: The study was a multi-center, open-label, prospective study. While specific countries are not mentioned, the manufacturer's address (Fremont, CA, USA) suggests it was likely conducted in the USA.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

• Number of Experts: Two independent reviewers (graders) were used.
• Qualifications of Experts: Both reviewers were licensed practitioners in optometry.

4. Adjudication Method for the Test Set

The reported method indicates that the best image out of two repeat fundus photos was selected for assessment. Although two reviewers graded the images to assess inter-rater agreement, the text does not explicitly detail a formal adjudication method (like 2+1 or 3+1 consensus) for discrepancies if they occurred. It states: "Agreement between PI and independent graders with regards to clinically usefulness of images was concluded to be at an acceptable level in all cases."

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Yes, a multi-reader multi-case (MRMC) comparative effectiveness study was performed. The study aimed to evaluate the non-inferiority of the iCam relative to the predicate (Centervue DRS) regarding image quality.

• Effect Size of Human Reader Improvement (AI vs. without AI assistance): The study design does not involve AI assistance to human readers. It's a direct comparison of images captured by two different devices (iCam vs. DRS), with human readers evaluating the image quality from both devices. Therefore, there's no data to report on how much human readers improve with AI vs. without AI assistance. The study focuses on the inherent quality of images produced by the iCam compared to the predicate, as judged by human readers.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

No, a standalone (algorithm only) performance study was not conducted or reported. The "effectiveness" of the iCam was evaluated by comparing its captured images to the predicate device, with physicians (licensed practitioners in optometry) assessing the clinical usefulness of the images. The iCam "provides images only and does not provide any diagnostic, pathological analysis or classification of ocular health or disease."

7. Type of Ground Truth Used

The ground truth for clinical usefulness was established by expert consensus (or at least expert grading with high inter-rater agreement). Specifically, a 5-point image quality grading scale was used by licensed optometry practitioners, and then dichotomized at a threshold of ≥3 for "clinically useful."

8. Sample Size for the Training Set

The document does not provide information about a separate training set or its sample size. This is a 510(k) for a medical imaging device (camera), not an AI algorithm that requires training data in the traditional sense. The performance evaluation focuses on the image acquisition capabilities of the camera.

9. How the Ground Truth for the Training Set Was Established

As no training set is mentioned for an AI algorithm, the method for establishing its ground truth is not applicable here. The "ground truth" in this context refers to the expert assessment of the images captured by the device itself.

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MiiS Horus Scope DEC 100 is a digital hand-held eye-fundus camera used to record digital photographs and video of the fundus of the human eye and surrounding area.

MilS Horus Scope DEC 100 is a digital hand-held eye-fundus camera used to record digital photographs and video of fundus of the human eye and surrounding area. It is more efficient and suitable for many different applications, such as telemedicine and electronic filing.

The provided text is a 510(k) summary for the MiiS Horus Scope DEC 100, a digital hand-held eye-fundus camera. This document focuses on establishing substantial equivalence to a predicate device and includes the device description, intended use, and a formal letter of clearance from the FDA.

Crucially, the document does not contain any acceptance criteria or details of a study proving the device meets acceptance criteria. It is a regulatory submission for market clearance, not a clinical study report. Therefore, I cannot provide the requested information.

The document states:

• "MiiS Horus Scope DEC 100 is substantially equivalent to the predicate device with respect to functionality, design verification, intended use and performance characteristics."
• "Based on the 510(k) summaries and the information provided herein, we conclude that the submitted device is substantially equivalent to the predicate device under the Federal Food, Drug, and Cosmetic Act."

This indicates that the submission focuses on demonstrating equivalence rather than presenting specific quantitative performance metrics against predefined acceptance criteria from a clinical study.

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The Non-Mydriatic Auto Fundus Camera AFC-330 with Image Filing Software NAVIS-EX is intended to capture, display, store and manipulate images of the retina and the anterior segment of the eye, to aid in diagnosing or monitoring diseases of the eye that may be observed and photographed.

The Non-Mydriatic Auto Fundus Camera AFC-330 with Image Filing Software NA VIS-EX ("AFC-330 with NA VIS-EX") is a conventional non-mydriatic auto fundus camera. The AFC-330 with NAVIS-EX captures fundus images using a built-in colour CCD camera without the use of mydriatic agents. With this single device, registration of patient information, image capture, and viewing of captured images are possible. By connecting a personal computer (PC) to the device via a LAN and installing the NA VIS-EX image filing system software, images captured by this device can be transferred to the PC and viewed and managed on the PC.

The provided text is a 510(k) summary for the Nidek Non-Mydriatic Auto Fundus Camera AFC-330 with Image Filing Software NAVIS-EX. It describes the device, its intended use, and substantial equivalence to predicate devices, but it does not contain information about acceptance criteria or a specific study proving the device meets those criteria, as typically found in clinical performance studies of AI/CADe devices.

The document states:

• Testing in support of substantial equivalence determination: "All necessary bench testing was conducted on the AFC-330 with NA VIS-EX to support a determination of substantial equivalence to the predicate devices. The performance testing included the following tests:
  • Electrical and mechanical safety testing
  • Electromagnetic compatibility testing
  • Light burden testing
  • Verification and validation testing"
• Summary: "The collective performance testing results demonstrate that AFC-330 with NA VIS-EX is substantially equivalent to the predicate devices."

This indicates that the submission relied on bench testing to demonstrate performance characteristics related to safety and fundamental functionality, rather than a clinical study evaluating diagnostic accuracy against specific performance metrics and acceptance criteria for an AI or CADe component. The device appears to be a traditional fundus camera with image filing software, not a product that performs automated diagnostic interpretations requiring acceptance criteria like sensitivity, specificity, or AUC based on expert reads.

Therefore, I cannot provide the requested information regarding acceptance criteria and studies proving the device meets them because such information is not present in the provided text. The submission focuses on demonstrating substantial equivalence through standard device testing (safety, EMC, light burden, verification/validation) for a medical imaging acquisition and management system.

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The OPTO Global Digital Fundus Camera System Model ADS 1.5 is intended to be used to capture, archive, recall and display black and white, and color images of the retina, as well as surrounding areas, to aid in the diagnosis and monitoring of diseases of the eye that may be viewed and photographed non-invasively.

The OPTO Global ADS 1.5 Digital Fundus Camera System is an automated imaging device used in conjunction with a digital camera that requires ninimal intervention during the capture of an image. The system is simple to use and requires minimal training for a user to become proficient with the system. Like the listed predicate devices, the Opto Global Digital Fundus Camera System Model ADS 1.5 is comprised of a digital imaging camera, computer hardware, and a software platform intended to be used to store images captured by the fundus camera.

The Opto Global Digital Fundus Camera System Model ADS 1.5 is comprised of the following components: A digital sensor head (digital camera) a computer interface circuit board (digital image capture card), and connecting cables. These components are then combined and sold together with our "OPTO Global Capture" proprietary imaging software and a computer (CPU) monitor, keyboard and mouse. This total system with imaging software provides acquisition and hardware control capabilities used to take digital pictures of the retina which are then transferred via the digital camera and connecting cable to the computer system transment can be viewed, modified, stored or printed.

The provided text does not contain detailed information about specific acceptance criteria and a study proving device performance in the way typically seen for AI/ML-based medical devices. This 510(k) summary is for a digital fundus camera system, which primarily focuses on image capture, archiving, and display, rather than an AI algorithm for diagnostic interpretation. The submission relies on substantial equivalence to predicate devices, rather than an independent performance study against defined acceptance criteria.

However, I can extract the information that is present and highlight what is missing based on your request.

Here's a breakdown:

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

The document does not explicitly define acceptance criteria as quantitative metrics (e.g., sensitivity, specificity, accuracy) nor does it report specific device performance against such metrics. The "performance characteristics" section describes the components and functions of the system.

Instead, the submission argues for substantial equivalence based on:

• Same intended use: "to capture, archive, and display digital images of the eye, particularly the retina obtained through the use of an ophthalmic camera (fundus camera)."
• Same indications for use: "to capture, archive, recall and display black and white, and color images of the retina, as well as surrounding areas, to aid in the diagnosis and monitoring of diseases of the eye that may be viewed and photographed non-invasively."
• Equivalent principles of operation and technological characteristics: Digital imaging camera, computer hardware, software platform for image storage.
• Similar basic software functions: image acquisition, storage, analysis, and retrieval.
• Similar operation manner: User (ophthalmologist or photographer) views the patient's eye through a fundus camera with a digital camera to capture, manipulate, and archive images.

The closest to "acceptance criteria" mentioned are:

• "OPTO Global Inc. has performed several software validation tests the results of which clearly indicate that the Opto Global Digital Fundus Camera System Model ADS 1.5 meets comparable system and software standards exhibited by the predicate devices listed." (No specific results or standards are provided.)
• The system allows the user to "monitor, capture, and process, images thus verifying the device is operating correctly."

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

• Not Applicable / Not Provided. This submission relies on substantial equivalence and software validation, not a clinical performance study with a test set of images.

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 Applicable / Not Provided. No ground truth for image interpretation or diagnosis was established, as the device is for image capture and display, not AI-driven interpretation.

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

• Not Applicable / Not Provided.

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. This is a digital fundus camera system, not an AI-assisted diagnostic tool.

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

• No. This is a digital fundus camera system, not a standalone AI algorithm. The performance is related to its ability to capture, store, and display images, with the user ("Ophthalmologist, or Photographer") in control.

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

• Not Applicable / Not Provided. The "ground truth" for this device's performance would likely be related to image quality metrics (resolution, clarity, color accuracy), storage integrity, and display accuracy, which are not detailed in this summary for substantial equivalence.

8. The sample size for the training set

• Not Applicable / Not Provided. This is not an AI/ML device that requires a training set of data.

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

• Not Applicable / Not Provided.

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