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The Axcel™ Surgical Light is a new product designation intended to identify a family of surgical lights that will use a similar set of design principals as the PrismAlix® (PRX) Series Surgical Light. The primary predicate device focuses on the PRX4000, which is the smaller lighthead within the PRX Series. Additionally, the Axcel light design also uses some design features from the Angenieux AX4 Surgical Light. Comparisons are made between it and these two predicate devices.

The current Axcel™ Family of Products and Configurations are shown in Table 1 below, along with their associated FDA Product Code(s). There are three configurations and comprise of two (2) ceiling mounted light system configurations (single and two lighthead) and one (1) wall mount system with one lighthead.

AI/ML Overview

This 510(k) summary for the ALM Axcel™ Surgical Light describes the device and its substantial equivalence to predicate devices, but it does not include information about specific acceptance criteria or an explicit study proving the device meets them, as it is understood in the context of performance metrics like sensitivity, specificity, or accuracy that are common in AI/ML medical devices.

Instead, the submission focuses on demonstrating compliance with safety standards and the substantial equivalence of modifications compared to existing predicate devices.

Therefore, the specific quantitative details requested in the prompt regarding acceptance criteria, performance metrics, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, or standalone algorithm performance are not available in the provided document.

However, I can extract information about the types of tests conducted and what the "acceptance criteria" can be inferred to be in this regulatory context.

Here's a breakdown of the available information based on your prompt:

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

Since this is a submission for a surgical light, the "acceptance criteria" are primarily related to safety, electrical standards, and functional equivalence to predicate devices, rather than performance metrics like sensitivity or specificity. The document doesn't provide a table of performance results against numerical acceptance criteria in the way an AI/ML device submission would.

Inferred "Acceptance Criteria" (based on standards compliance and substantial equivalence argument):

Inferred "Acceptance Criteria" Category	Description	Reported Device Performance (as stated in the document)
Safety and Electrical Standards	Compliance with recognized national and international safety standards for medical electrical equipment and surgical luminaires.	"The test data supports conformance to:UL 2601-1 Standard for Medical Electrical Equipment, Part 1: General Requirements for SafetyCSA C22.2 No. 601.1 Medical Electrical Equipment, Part 1: General Requirements for SafetyIEC 60601-2-41 Medical electrical equipment Part 2-41: Particular requirements for the safety of surgical luminaires and luminaires for diagnosticsEN 60601-1-2 Medical electrical equipment Part 1-2: General requirements for safety - Collateral standard: Electromagnetic compatibility - Requirements and tests"
Software Safety Level	Determination that software poses a minor level of concern.	"Software used in the Axcel™ Surgical Light was tested according to the appropriate FDA Software Guidance Documents, per its determination as a Minor Level of Concern."
Functional Equivalence	The modified design and features are functionally equivalent to the predicate devices and do not raise new questions of safety or effectiveness. (This is the overarching "acceptance criterion" for a 510(k) pathway).	"The modifications incorporated into the Axcel™ Surgical Light designs used those desired design features from both the ALM PrismAlix® (PRX4000 Series) and Angenieux AX4 Surgical Lights. Based upon the information provided herein this 510(k) Premarket Notification, we conclude that the ALM Axcel™ Surgical Light is substantially equivalent to the predicate device(s) and is safe and effective when used as intended."
Intended Use	The device fulfills its stated intended use.	"The Axcel™ Surgical Lights are intended to be used to provide visible illumination of the surgical area or the patient." (This use is considered equivalent to predicates).

2. Sample sized 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 in the context of this device and submission type. The testing was for compliance with engineering and electrical safety standards, not a statistical performance test against a "test set" of medical data.
Data Provenance: Not applicable. The "data" here refers to test results from engineering and safety assessments, not clinical data.

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. Ground truth, in the sense of expert consensus on medical findings, is not relevant for a surgical light's safety and electrical compliance testing. The "ground truth" here is the adherence to engineering standards.

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

Not applicable. This type of adjudication is for resolving discrepancies in expert interpretations of medical data, which is not part of this submission.

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 study is not relevant for a surgical light. This device is not an AI/ML diagnostic tool or an aid for human interpretation of medical cases.

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

No. This device is a physical surgical light, not an algorithm. While it contains "Software... determined as a Minor Level of Concern," the submission does not detail standalone performance of this software in a clinical context. Its function is to control light intensity and on/off, not to make diagnostic or therapeutic decisions.

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

The "ground truth" in this context is the established engineering and electrical safety standards (e.g., UL 2601-1, CSA C22.2 No. 601.1, IEC 60601-2-41, EN 60601-1-2) and FDA guidance for software. The device's performance is measured against these technical specifications and regulatory requirements.

8. The sample size for the training set

Not applicable. 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.

Summary regarding "the study that proves the device meets the acceptance criteria":

The "study" proving the device meets the acceptance criteria, in this context, consists of nonclinical testing for conformance to recognized safety and electrical standards, along with a comparative analysis of its design and features against legally marketed predicate devices. The conclusion rests on demonstrating that the device is substantially equivalent to existing devices and adheres to relevant safety regulations. The formal "approval" is the FDA's 510(k) clearance letter.

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