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This information can be complemented by the imaging information from optical impression systems. The additional information about the exact geometry of the tooth surfaces can be visualized together with the radiological data.

HICAT Air is also used as a software system to aid qualified medical professionals with the diagnosis, and followed by the evaluation, comparison and planning of ENT treatment options.

The medical professionals' input information and planning data may be exported from HICAT Air to be used as input data for CAD or Rapid Prototyping Systems for the manufacturing of therapeutic devices such as oral devices.

AI/ML Overview

Here's a breakdown of the requested information based on the provided text. Unfortunately, much of the detailed quantitative information for acceptance criteria and study particulars for HICAT Air is not explicitly stated in this 510(k) summary. The document focuses on demonstrating substantial equivalence to predicate devices rather than providing a detailed performance study like a clinical trial.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list specific quantitative acceptance criteria (e.g., minimum accuracy percentages, sensitivity, specificity) for the device's performance that were "met." Instead, it relies on demonstrating that HICAT Air's capabilities are comparable to or an improvement upon the predicate devices, particularly for visualization, segmentation accuracy, and measurement accuracy.

Below is a table summarizing the reported device performance as described in the comparison to predicate devices, which can be inferred as the "criteria" it aims to meet or exceed based on the predicates.

Feature / Metric	Acceptance Criteria (Implied / Predicate-based)	Reported HICAT Air Performance
Overall Length Measurement Accuracy	100 µm (based on Primary Predicate: SICAT Function)	100 µm (Algorithms identical to SICAT Function)
Overall Angular Measurement Accuracy	1 degree (based on Primary Predicate: SICAT Function)	1 degree (Algorithms identical to SICAT Function)
Segmentation Algorithms	Segmentation of anatomical structures using Water Shed (graph-cut algorithm) (based on Primary Predicate: SICAT Function) and segmentation for Dolphin Imaging 11.5 (algorithm unknown but present). Airway segmentation present in Dolphin Imaging 11.5.	Yes, using a segmentation wizard. Algorithm: Water Shed (a type of graph-cut algorithm), identical to SICAT Function. Airway Segmentation of the Airway using the segmentation wizard.
Airway Analysis (volume, cross-section, min cross-section)	Present in Reference Predicate: Dolphin Imaging 11.5 (analyze airway by drawing border, program fills and displays airway space, reports volume, locates and measures most constricted spot).	Yes (Calculation of airway volume, airway cross section dimensions, position and size of the area with minimum cross section in airway).
Imaging Data Visualization Region	ENT region including the oral-maxillofacial region (based on Reference Predicate: Dolphin Imaging 11.5) and oral-maxillofacial region (based on Primary Predicate: SICAT Function).	ENT region including the oral-maxillofacial region.
Safety and Effectiveness	Demonstrated through verification and validation activities and substantial equivalence to predicate devices, ensuring no adverse impact from minor differences.	All verification and validation activities passed; safety and effectiveness demonstrated in context of indications for use.

Note: The phrase "Algorithms identical to SICAT Function" is frequently used, implying that the performance metrics of SICAT Function are directly applicable to HICAT Air for those features.

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

Test Set Sample Size: The document mentions "Special bench testing has been performed with non-clinical data" to verify segmentation performance and calculation of geometric dimensions. However, it does not specify the sample size for this test set.
Data Provenance: The document states "non-clinical data" was used for special bench testing. There is no information provided on the country of origin of this data, nor if it was retrospective or prospective. It implies simulated or idealized data given the "non-clinical" description.

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

The document does not specify the number of experts used to establish ground truth for the test set, nor does it detail their qualifications. The tests appear to be bench tests against pre-defined or simulated "ground truth" rather than expert-derived ground truth on clinical cases.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method (e.g., 2+1, 3+1, none) for the test set. Given the "non-clinical data" and "bench testing" description, it's likely that a human adjudication process for complex clinical opinions was not part of this specific testing.

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

An MRMC comparative effectiveness study was not explicitly described in the provided text. The submission focuses on demonstrating substantial equivalence through comparison of features, technical characteristics, and performance metrics (like measurement accuracy) to predicate devices, along with internal verification and validation activities. There is no mention of human readers improving with or without AI assistance, or any effect size calculation.

6. Standalone Performance Study

A standalone performance study (algorithm only without human-in-the-loop performance) was performed. The "Special bench testing" mentioned falls under this category, focusing on "segmentation performance of anatomical structures of the airway" and "correct calculation of geometric dimensions of the airway by the airway analysis tool." These tests aim to verify the algorithm's capabilities independently.

7. Type of Ground Truth Used (for standalone testing)

For the standalone "Special bench testing," the ground truth likely involved known or simulated anatomical structures with precise geometric dimensions to verify segmentation and measurement accuracy. The term "non-clinical data" supports this interpretation, suggesting synthetic data, phantoms, or highly characterized datasets where definitive measurements and segmentations are pre-established rather than derived from expert consensus on complex, variable clinical cases or pathology.

8. Sample Size for the Training Set

The document does not provide any information regarding a training set sample size. As HICAT Air is described primarily as a "radiological visualization software for diagnosis and treatment planning" with identical algorithms to a predicate for many features, and its segmentation logic is based on a "Water Shed (a type of graph-cut algorithm)," it is possible that it does not involve a machine learning model that requires a "training set" in the conventional sense, or if it does, the details are not included in this summary.

9. How Ground Truth for the Training Set Was Established

Since no training set is mentioned or implied for a machine learning model that would require one, there is no information provided on how ground truth for a training set was established.

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