(157 days)
The Digital Prism Correction Feature (DPCF) is software that is intended to provide digital image adjustments in Apple Vision Pro in accordance with a user's prism prescription.
DPCF is available over-the-counter (OTC) for users with prism in their eyeglass prescription. When a prescription also includes other parts (e.g., sphere, cylinder, ADD), which can be fulfilled by optical inserts, the DPCF fulfills the prism part of the prescription while using Apple Vision Pro.
DPCF supports prism prescriptions up to 7.75 Prism Diopters (PD) in the horizontal and/or vertical dimension (i.e., base-up, base-down, base-in, base-out), per eye.
The Digital Prism Correction Feature (DPCF) is intended to provide a high quality visual experience in Apple Vision Pro spatial computing applications for users with a prism prescription. Specifically, the DPCF is software that is intended to provide digital image adjustments in Apple Vision Pro in accordance with a user's prism prescription, in the horizontal and/or vertical dimensions. DPCF fulfills the prism part of an eyeglass prescription. When an eyeglass prescription also includes other parts (e.g., sphere, cylinder, ADD), DPCF fulfills the prism part of the prescription, while prescription optical inserts fulfill the other parts of the prescription.
The DPCF achieves its intended use by converting a user's prism prescription into digital image adjustment parameters that are utilized by the spatial computing image system to automatically provide digital image adjustments in horizontal and/or vertical dimensions in accordance with a user's prism prescription. At this time, DPCF supports prism prescriptions up to 7.75 Prism Diopters (PD) in the horizontal and/or vertical dimensions (i.e, base-up, base-down, base-in, base-out), per eye.
The DPCF is available over-the-counter (OTC).
The provided FDA 510(k) clearance letter and summary for the Apple Digital Prism Correction Feature (DPCF) primarily discuss its substantial equivalence to a predicate device and its move from prescription to over-the-counter (OTC) use. It does not contain an in-depth study proving the device meets acceptance criteria in the typical sense of a clinical trial for diagnostic AI.
However, based on the information provided, we can extract details about the acceptance criteria and the type of study conducted to support the device's performance, particularly focusing on the "Summary of Non-Clinical Testing."
Here's an analysis of the requested information:
Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Target) | Reported Device Performance |
|---|---|
| Meets standardized prism tolerance requirements (ISO 8980-1:2017) | "demonstrated DPCF meets prism tolerance requirements specified in ISO 8980-1:2017" |
| Acceptable use-related risks for OTC use | "demonstrate that the use-related risks are acceptable" |
Study Details
2. Sample size used for the test set and the data provenance:
- Test Set Sample Size: For the human factors and usability study, the sample size was 30 subjects.
- Data Provenance: The document does not explicitly state the country of origin. The study was conducted as non-clinical testing to assess "self-selection and use-related risks associated with use of the DPCF as an OTC device." The nature of "bench validation testing" mentioned for prism tolerance requirements suggests it's a controlled engineering test rather than a patient data-driven study.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- The document does not specify the number or qualifications of experts involved in establishing ground truth for the human factors study. For “bench validation testing” which measured prism tolerance, the ground truth would be based on metrological standards and precision instruments, rather than expert judgment.
4. Adjudication method for the test set:
- The document does not describe any adjudication method. The human factors study assessed self-selection and use-related risks, which would typically involve observing user interactions and collecting feedback, rather than a diagnostic accuracy adjudication process. The bench testing involves direct measurement against a standard.
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, an MRMC comparative effectiveness study was not explicitly mentioned or performed. The DPCF is a "Digital Prism Correction Feature" designed to "provide digital image adjustments" based on a user's existing prism prescription. It's not a diagnostic AI device that assists human readers in interpreting medical images or data. Therefore, the concept of "human readers improving with AI vs without AI assistance" does not directly apply to the described function of this device, which seems to be a corrective rather than diagnostic tool.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
- Yes, in essence. The "bench validation testing that demonstrated DPCF meets prism tolerance requirements specified in ISO 8980-1:2017" represents a standalone evaluation of the algorithm's output (the digital image adjustment) against a predefined standard, independent of human interaction for interpretation. The human factors study, while involving humans, evaluates the usability and safety of the interface for an OTC product, not the diagnostic performance of an algorithm.
7. The type of ground truth used:
- For prism tolerance requirements: The ground truth is based on standardized metrological requirements as specified in ISO 8980-1:2017. This is a technical standard for ophthalmic optics.
- For human factors and usability: The "ground truth" would be the assessment of use-related risks against predefined safety and usability thresholds, determined through observation and feedback collection in a controlled user study.
8. The sample size for the training set:
- The document does not provide information regarding a training set sample size. This is consistent with the device being a "Digital Prism Correction Feature" that applies a known optical principle (prism correction) digitally, rather than a machine learning model trained on a large dataset to perform a diagnostic or predictive task. The device likely uses algorithms based on physics and geometry to implement the prism correction, rather than being a trained AI model in the typical sense.
9. How the ground truth for the training set was established:
- As no training set is mentioned in the context of machine learning, there is no information provided on how ground truth for a training set was established. The device functions as a digital implementation of an established optical correction principle.
Summary of Device Functionality Context: The DPCF appears to be a software feature that digitally applies prism correction within the Apple Vision Pro. Its "AI" component, if any, is not a diagnostic or predictive model in the typical sense seen in many FDA-cleared AI pathology or radiology devices. Instead, it seems to be a precise digital enactment of a known optical principle to correct for existing prism prescriptions. The studies described focus on whether this digital implementation meets optical accuracy standards and whether its use as an over-the-counter device is safe and usable.
§ 886.1655 Ophthalmic Fresnel prism.
(a)
Identification. An ophthalmic Fresnel prism is a device that is a thin plastic sheet with embossed rulings which provides the optical effect of a prism. The device is intended to be applied to spectacle lenses to give a prismatic effect.(b)
Classification. Class I (general controls). The device is exempt from the premarket notification procedures in subpart E of part 807 of this chapter, subject to the limitations in § 886.9. The device is also exempt from the current good manufacturing practice requirements of the quality system regulation in part 820 of this chapter, with the exception of § 820.180, with respect to general requirements concerning records, and § 820.198, with respect to complaint files.