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The SPECTRALIS with Flex Module is a non-contact ophthalmic diagnostic imaging device intended to aid in the visualization of the posterior segment structures of the eye and vasculature of the retina and choroid. SPECTRALIS with Flex Module is intended for imaging of adults and pediatric patients in supine position.

The Heidelberg Engineering SPECTRALIS with Flex Module is based on the predicate SPECTRALIS HRA+OCT consisting of an accessory device mount allowing imaging of patients in supine position. The SPECTRALIS with Flex Module is intended for visualization of the posterior segments of the human eye. The SPECTRALIS with Flex Module is using the identical technologies as the predicate SPECTRALIS tabletop configuration (K223557), i.e. it is a combination of a confocal laser-scanning ophthalmoscope (cSLO, the HRA portion) and a spectral-domain optical coherence tomographer (SD-OCT).

Here's the breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Device Name: SPECTRALIS with Flex Module
Predicate Device: SPECTRALIS HRA+OCT (K223557)

• 100% of OCT images were assessed as having sufficient overall image quality.
• 100% of IR cSLO images were assessed as having sufficient overall image quality.
• 97.2% of OCTA images were assessed as having sufficient overall image quality. (Page 14) |
  | Visibility of Key Anatomic Structures | Key anatomic structures should be clinically acceptably visualized. | In the effectiveness population (All Eyes):
• Clinically acceptable visualization of key anatomic structures was achieved on OCT in 100% of assessments.
• On OCTA, clinically acceptable visualization was achieved in 94.4% to 97.2% of assessments. (Page 14)
  The results generally demonstrate that investigational SPECTRALIS with Flex images provide similar visualization as compared to the predicate SPECTRALIS. (Page 14-15) |
  | Identification of Pre-specified Abnormalities | The device should have comparable ability to identify pre-specified abnormalities (structural via OCT, vascular via OCTA) compared to the predicate device. | - Agreement rates (between investigational device and predicate for same result):
  • Structural abnormalities on OCT: ≥ 84.3%.
  • Vascular abnormalities on OCTA: ≥ 85.1%.
• Negative Percent Agreement (NPA) in All Eyes for all pre-specified abnormalities:
  • On OCT: at least 87.8%.
  • On OCTA: at least 92.6%.
• Positive Percent Agreement (PPA) for All Eyes for all pre-specified abnormalities (with more than 2 cases identified on the predicate):
  • On OCT: at least 75%.
  • On OCTA: at least 75%.
    These results indicate the ability to identify each pre-specified abnormality is similar between devices. (Page 15) |
    | Safety | The device should not introduce new safety concerns. | No adverse events occurred during the course of the study. (Page 14) |
    | Substantial Equivalence (General) | The device should be as safe and effective as the predicate devices. | The study concludes that the SPECTRALIS with Flex Module is substantially equivalent to the predicate SPECTRALIS with regards to image quality, visibility of key anatomic structures, and identification of structural and vascular abnormalities, and supports its safety. (Page 15) |
    | Supine & Pediatric Patients | Demonstrate effective and safe imaging for pediatric and adult patients in the supine position. | The clinical study included adult patients (22 years or older) and the literature review provides evidence for safe and effective use in pediatric conscious or unconscious patients in the supine position. The device shares the patient profile (conscious/unconscious pediatrics and adults in supine position) with the secondary predicate device (Bioptigen ENVISU). (Page 15-16) |

Study Details

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

   • Subjects Enrolled: 88
   • Subjects Completed Study: 84
   • Effectiveness Analysis Population: 76 participants (25 Normal subjects, 51 Posterior Segment Abnormality subjects). The exact count varied slightly for each image type based on abnormalities of interest and acceptable acquisitions.
   • Data Provenance: Single clinical site, located in the United States. The study was prospective and observational.
   • Patient Demographics:
     • Mean age: 57.3 ± 19.2 years (overall)
     • Gender: 59.2% female, 40.8% male
     • Ethnicity: 96.1% did not identify as Hispanic or Latino
     • Race: 80% White, 18.4% Black/African American, 2.6% Asian
   • Study Populations:
     • Adult Normal Eyes (no posterior segment abnormalities)
     • Adult Posterior Segment Abnormality Eyes (structural and/or vascular posterior segment abnormalities)

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

   • Number of Experts: Three independent readers.
   • Qualifications: Referred to as "independent readers from a reading center." No specific qualifications (e.g., years of experience, subspecialty) are provided in this extract.

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

   • The text states that the "proportion of images graded better than Poor on consensus" was used. For abnormality identification, "agreement rates (based on the abnormalities identified by the reading center on the predicate, percentage of eyes with the same result on the investigational device)" were utilized. This implies that the three readers likely had a consensus process for the overall image quality and visibility assessments, and for abnormality identification, agreement with the predicate's findings by the reading center was important. The specific 2+1 or 3+1 method is not explicitly mentioned, but the term "consensus" suggests an agreed-upon finding among the readers.

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:

   • This was not an MRMC comparative effectiveness study involving AI assistance. The study was designed to show substantial equivalence between the investigational device (SPECTRALIS with Flex Module) and its predicate (SPECTRALIS HRA+OCT) regarding image quality and ability to identify abnormalities. There is no mention of AI or its impact on human reader performance.

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

   • No, this was not a standalone (algorithm-only) study. The study involved human readers (three independent readers) to grade images obtained from the device.

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

   • The ground truth for image quality, visibility of structures, and identification of abnormalities was established through expert grading by three independent readers from a reading center, likely using expert consensus to interpret findings. The predicate device's findings also served as a reference for agreement in abnormality detection.

7. The sample size for the training set:

   • The provided text does not contain information about a training set size. The clinical study described is for validation and comparison to a predicate, not for training a new algorithm.

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

   • As no training set is mentioned, this information is not applicable based on the provided document.

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The SPECTRALIS is a non-contact ophthalmic diagnostic imaging device. It is intended for:

• · viewing the posterior segment of the eye, including two- and three- dimensional imaging
• · cross-sectional imaging (SPECTRALIS HRA+OCT and SPECTRALIS OCT)
• fundus imaqinq
• · fluorescence imaging (fluorescein angiography, indocyanine green angiography; SPECTRALIS HRA+OCT, SPECTRALIS HRA)
• autofluorescence imaging (SPECTRALIS HRA+OCT, SPECTRALIS HRA and SPECTRALIS OCT with BluePeak)
• · performing measurements of ocular anatomy and ocular lesions.

The device is indicated as an aid in the detection and management of various ocular diseases, including:

• age-related macular degeneration
• macular edema
• · diabetic retinopathy
• retinal and choroidal vascular diseases
• glaucoma

The device is indicated for viewing geographic atrophy.

The SPECTRALIS OCT Angiography Module is indicated as an aid in the visualization of vascular structures of the retina and choroid.

The SPECTRALIS HRA+OCT and SPECTRALIS OCT include the following reference databases:
• a retinal nerve fiber layer thickness reference database, which is used to quantitatively compare the retinal nerve fiber layer in the human retina to values of Caucasian normal subjects – the classification result being valid only for Caucasian subjects
• a reference database for retinal nerve fiber layer thickness and optic nerve head neuroretinal rim parameter measurements, which is used to quantitatively compare the retinal nerve fiber layer and neuroretinal rim in the human retina to values of normal subjects of different races and ethnicities representing the population mix of the USA (Glaucoma Module Premium Edition)

The Heidelberg Engineering SPECTRALIS HRA+OCT is a device used to image the anterior and posterior segments of the human eye. The SPECTRALIS HRA+OCT is a combination of a confocal laser-scanning ophthalmoscope (cSLO, the HRA portion) and a spectral-domain optical coherence tomographer (SD-OCT). The confocal laser- scanning part of the device allows for acquisition of reflectance images (with blue, green or infrared light), conventional angiography images (using fluorescein or indocyanine green dye) and autofluorescence images. The different imaging modes can be used either alone or simultaneously. The SD-OCT part of the device acquires cross-sectional and volume images, together with an HRA cSLO image.

A blue laser is used for fluorescein angiography, autofluorescence imaging, and blue reflectance imaging, and two infrared lasers are used for indocyanine green angiography and infrared reflectance imaging. A green laser is used for MultiColor imaging ("composite color images"). MultiColor imaging is the simultaneous acquisition of infrared, green and blue reflectance images that can be viewed separately or as a composite color image. For SD-OCT imaging, an infrared superluminescent diode and a spectral interferometer are used to create the crosssectional images.

The following modifications have been applied to the device subject of this 510(k):

• . Addition of scan acquisitions for the SPECTRALIS OCT Angiography Module (OCTA) at 125 kH
• Addition of a General-Purpose Graphics Processing Unit (GPGPU)

Here's a breakdown of the acceptance criteria and study details based on the provided FDA 510(k) summary:

Device: SPECTRALIS HRA+OCT and variants

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary doesn't explicitly state "acceptance criteria" with numerical thresholds in the typical sense for a pass/fail. Instead, it demonstrates similarity and non-inferiority to a predicate device. The performance metrics reported serve as evidence that the new modifications do not negatively impact the device's functionality compared to the predicate.

Overall Conclusion from Study: The investigational SPECTRALIS OCTA images provide similar visibility as compared to the predicate (85 kHz) and the ability to identify each pre-specified vascular abnormality is similar between the predicate and investigational scan types.

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

• Sample Size (Effectiveness Analysis Population): 79 subjects. However, the exact count for direct comparison between the predicate and investigational device varied depending on the scan type.
• Data Provenance:
  • Country of Origin: United States
  • Retrospective or Prospective: Prospective
  • Study Design: Observational Case Study (S-2020-5)

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

• Number of Experts: Three independent reviewers.
• Qualifications of Experts: The document states they were "from a reading center" but does not specify their individual qualifications (e.g., "radiologist with 10 years of experience").

4. Adjudication Method for the Test Set

The document explicitly states that the analyses were based on "the grading results from the effectiveness analysis population," and imagery was "graded better than Poor on consensus." This implies a consensus-based adjudication method for image quality and visualization of structures, and agreement analysis for vascular abnormalities. It does not specify a 2+1 or 3+1 rule, but highlights the "consensus" aspect.

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

No, a typical MRMC comparative effectiveness study, designed to measure how much human readers improve with AI vs. without AI assistance, was not conducted. This study's primary goal was to demonstrate that modifications to an existing device (adding new scan acquisitions, increasing scan speed, and GPU processing) did not negatively impact its performance compared to its predicate. It assesses the similarity of the device's output (images) between the investigational and predicate versions.

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

Since the device in question is an imaging device (OCT) that captures and processes images for clinical evaluation by a human, and the study involves human graders assessing image quality and identifying pathologies, a "standalone algorithm-only" performance evaluation (without human-in-the-loop) in terms of clinical interpretation was not the primary focus or design of this particular study. The assessment revolves around the quality of the device's output for human interpretation.

7. Type of Ground Truth Used

The ground truth was established by expert consensus from the three independent reviewers from a reading center, who graded the OCTA scans on image quality, visibility of key anatomical vascular structures, and identification of pathologies.

8. Sample Size for the Training Set

The document does not provide information about a training set. This study is a clinical performance evaluation of an updated imaging device, not typically a machine learning model that requires a discrete training set for its core function. The modifications involve hardware (scan speed, GPU) and an investigational scan type, and the performance assessment is against a predicate device.

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

As no training set is mentioned or implied for this type of device modification study, this information is not applicable.

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