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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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OptoMedical Technologies OCT-Camera is intended to acquire, process, display and save depth-resolved images of ocular tissue microstructure using Spectral Domain Optical Coherence Tomography (SD-OCT). The OCT-Camera is indicated for the use as an aid in the diagnosis of physiologic conditions of the eye through non-contact optical imaging of the various tissues of the eye is supported through the use of interchangeable lenses. It is indicated for use on patient populations from premature and neonatal infants to adult, and is suitable for patients ambulatory or confined. The system is indicated for use in supine imaging, mounted to a surgical microscope HS Hi-R NEO 900A NIR (Haag-Streit), and is suited for imaging patients under anesthesia.

The OCT-Camera can be attached to the camera port of surgical microscopes. The OCT-Camera is completely integrated into the surgical procedure by enabling the OCT imaging before, during, and after microsurgery without disrupting the microscopic view. The individual steps and the outcome of the surgical procedures, such as transplantation of the thin membranes or micro implants, are visualized in real time.

The OCT-Camera by OptoMedical Technologies GmbH facilitates the intraoperative use of OCT (iOCT). It is called OCT-Camera because it can be attached to the camera port of an operating microscope like any common camera that are used for the purpose of providing live view images of the surgical field.

The provided text is a 510(k) summary for the OptoMedical Technologies OCT-Camera. It describes the device, its intended use, and argues for its substantial equivalence to a predicate device, the Bioptigen Envisu™ SDOIS.

However, the document states that NO clinical performance data was required or performed for this device (OCT-Camera). Therefore, the device inherently does not have acceptance criteria or a study that proves it meets acceptance criteria in the way you've requested regarding performance metrics like accuracy, sensitivity, or specificity, which would typically come from clinical evaluations.

The "acceptance criteria" discussed in this document refer to the device meeting specific non-clinical requirements (e.g., electrical safety, compliance with laser safety standards, and overall design and performance functionality) to demonstrate substantial equivalence to the predicate device, not its diagnostic performance against a ground truth.

Here's a breakdown of the requested information based on the provided document:

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

Since no clinical performance study was conducted to establish diagnostic acceptance criteria (e.g., sensitivity, specificity, accuracy), this table cannot be populated with such metrics. The acceptance criteria and "performance" are framed in terms of meeting regulatory and safety standards, and demonstrating substantial equivalence to a predicate device through non-clinical testing.

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 for test set: Not applicable (no clinical testing was performed).
• Data provenance: Not applicable.

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)

• Number of experts: Not applicable.
• Qualifications of experts: Not applicable.

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

• Adjudication method: 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

• MRMC study: No. The device is not an AI-assisted diagnostic tool discussed in this context; it's an imaging device. The document explicitly states no clinical testing was required or performed.
• Effect size: Not applicable.

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

• Standalone performance: No. The device is not an algorithm, but an imaging camera for human use. No clinical performance testing against ground truth was conducted.

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

• Type of ground truth: Not applicable. No clinical ground truth was established as no clinical studies were performed.

8. The sample size for the training set

• Sample size for training set: Not applicable. The device is not described as an AI/ML algorithm that requires a training set in the context of diagnostic performance studies. Non-clinical testing was performed for safety and effectiveness, but this doesn't involve a "training set" in the AI sense.

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

• Ground truth for training set: Not applicable.

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