OTOPLAN is intended to be used by otologists and neurotologists as a software interface allowing the display, segmentation, and transfer of medical image data from medical CT, MR, and XA imaging systems to investigate anatomy relevant for the preoperative planning and postoperative assessment of otological and neurotological procedures (e.g., cochlear implantation).

Device Description

OTOPLAN is a Software as a medical Device (SaMD) which consolidates a DICOM viewer, ruler function, and calculator function into one software platform. The user can

import DICOM-conform medical images, fuse supported images and view these images.
navigate through the images and segment ENT relevant structures (semi-automatic/automatic), which can be highlighted in the 2D images and 3D view.
use a virtual ruler to geometrically measure distances and a calculator to apply established formulae to estimate cochlear length and frequency.
create a virtual trajectory, which can be displayed in the 2D images and 3D view.
identify electrode array contacts, lead, and housing of a cochlear implant to assess electrode insertion and position.
input audiogram-related data that were generated during audiological testing with a standard audiometer and visualize them in OTOPLAN.

OTOPLAN allows the visualization of third-party information, that is, cochlear implant electrodes, implant housings and audio processors.

The information provided by OTOPLAN is solely assistive and for the benefit of the user. All tasks performed with OTOPLAN require user interaction; OTOPLAN does not alter data sets but constitutes a software platform to perform tasks that are otherwise performed manually. Therefore, the user is required to have clinical experience and judgment.

AI/ML Overview

The provided document describes the acceptance criteria and the study that proves the device (OTOPLAN version 3.1) meets these criteria for several new functionalities.

Here's the breakdown:

Acceptance Criteria and Device Performance Study for OTOPLAN v3.1

1. Table of Acceptance Criteria and Reported Device Performance

The document describes performance tests for several new automatic functions introduced in OTOPLAN v3.1. These are broadly categorized into Temporal Bone, Skin, and Inner Ear segmentation and thickness mapping, and CT-CT and CT-MR Image Fusion.

Table: Acceptance Criteria and Reported Device Performance

Functionality Tested	Acceptance Criteria	Reported Device Performance	Pass/Fail
Temporal Bone Thickness Mapping	Mean Absolute Difference (MAD) ≤ 0.6 mm, 95% Confidence Interval (CI) upper limit ≤ 0.8 mm	MAD: 0.17–0.20 mm, CI: 0.19–0.22	Pass
Temporal Bone 3D Reconstruction	Mean DICE coefficient ≥ 0.85, 95% CI lower limit ≥ 0.85	DICE coefficient (R1): 0.88 [CI: 0.87–0.89]DICE coefficient (R2): 0.86 [CI: 0.85–0.87]DICE coefficient (R3): 0.89 [CI: 0.88–0.90]	Pass
Skin Thickness Mapping	Mean Absolute Difference (MAD) ≤ 0.6 mm, 95% Confidence Interval (CI) upper limit ≤ 0.8 mm	MAD: 0.21–0.23 mm, CI: 0.23–0.26	Pass
Skin 3D Reconstruction	Mean DICE coefficient ≥ 0.68, 95% CI lower limit ≥ 0.68	DICE coefficient (R1): 0.89 [CI: 0.88–0.90]DICE coefficient (R2): 0.87 [CI: 0.86–0.88]DICE coefficient (R3): 0.86 [CI: 0.84–0.88]	Pass
Scala Tympany 3D Reconstruction	Mean DICE coefficient ≥ 0.65, 95% CI lower limit ≥ 0.65	DICE coefficient: 0.76 [CI: 0.75–0.77]	Pass
Inner Ear (Cochlea, Semi-circular canals, internal auditory canal) 3D Reconstruction (CT)	Mean DICE coefficient ≥ 0.80, 95% CI lower limit ≥ 0.80	DICE coefficient (R1): 0.82 [CI: 0.81–0.83]DICE coefficient (R2): 0.84 [CI: 0.83–0.85]DICE coefficient (R3): 0.85 [CI: 0.84–0.86]	Pass
Inner Ear (Cochlea, Semi-circular canals, internal auditory canal) 3D Reconstruction (MR)	Mean DICE coefficient ≥ 0.80, 95% CI lower limit ≥ 0.80	DICE coefficient (R1): 0.81 [CI: 0.80–0.82]DICE coefficient (R2): 0.83 [CI: 0.82–0.84]DICE coefficient (R3): 0.84 [CI: 0.83–0.85]	Pass
Cochlear Parameters (CT)	Mean absolute error (MAE) CDLoc measurement ≤ 1.5 mm	MAE (±SD) for CDLoc:R1: 0.59 ± 0.37 mmR2: 0.64 ± 0.44 mmR3: 0.62 ± 0.39 mm	Pass
Cochlear Parameters (MR)	Mean absolute error (MAE) CDLoc measurement ≤ 1.5 mm	MAE (±SD) for CDLoc:R1: 0.56 ± 0.42 mmR2: 0.70 ± 0.39 mmR3: 0.64 ± 0.43 mm	Pass
Image Fusion (CT-CT) - Semitones	Maximum mean absolute semitone error per electrode contact < 7.0 semitones	Max semitone error (per rater): R1: 5.34, R2: 4.43, R3: 4.20	Pass
Image Fusion (CT-CT) - Landmark Distances	Mean point distance error at each anatomical landmark per rater must be < 0.88 mm	RWP: 0.49–0.51 mm, LWP: 0.53–0.66 mm, IWP: 0.47–0.52 mm, SWP: 0.42–0.53 mm	Pass
Image Fusion (CT-MR) - Semitones	Maximum mean absolute semitone error per electrode contact < 7.0 semitones	Max semitone error (per rater): R1: 3.94, R2: 3.90, R3: 3.97	Pass
Image Fusion (CT-MR) - Landmark Distances	Mean point distance error at each anatomical landmark per rater must be < 1.25 mm	RWP: 0.82–0.84 mm, LWP: 0.68–0.85 mm, IWP: 0.63–0.74 mm, SWP: 0.63–0.76 mm	Pass

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

Temporal Bone Thickness Mapping and Skin Thickness Mapping:
- Test set: 43 temporal bones (29 patients)
- Data provenance: Pooled from 4 clinical sites (retrospective, implied clinical data).
Temporal Bone 3D Reconstruction and Skin 3D Reconstruction:
- Test set: 31 temporal bones (23 patients)
- Data provenance: Pooled from 4 clinical sites (retrospective, implied clinical data).
Scala Tympany 3D Reconstruction:
- Test set: 450 clinical-resolution CBCT datasets derived from 75 cochleae
- Data provenance: Not explicitly stated beyond "clinical-resolution CBCT datasets".
Inner Ear (Cochlea, Semi-circular canals, internal auditory canal) 3D Reconstruction (CT):
- Test set: 44 ears (27 patients)
- Data provenance: Pooled from 1 clinical site (retrospective, implied clinical data).
Inner Ear (Cochlea, Semi-circular canals, internal auditory canal) 3D Reconstruction (MR):
- Test set: 41 ears (24 patients)
- Data provenance: Pooled from 4 clinical sites (retrospective, implied clinical data).
Cochlear Parameters (CT):
- Test set: 61 ears (53 patients)
- Data provenance: Pooled from 4 clinical sites (retrospective, implied clinical data).
Cochlear Parameters (MR):
- Test set: 63 ears (52 patients)
- Data provenance: Pooled from 4 clinical sites (retrospective, implied clinical data).
Image Fusion (CT-CT):
- Test set: 32 temporal bones (32 patients)
- Data provenance: Pooled from 4 clinical sites (retrospective, implied clinical data).
Image Fusion (CT-MR):
- Test set: 31 temporal bones (25 patients)
- Data provenance: Pooled from 4 clinical sites (retrospective, implied clinical data).

General Note on Data Provenance: The document consistently refers to data being "Pooled (X clinical sites)". This implicitly suggests the data is retrospective patient data from various clinical centers, but specific countries of origin and the exact prospective/retrospective collection mechanism are not detailed beyond this.

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

General across all segmentation/measurement tasks: Three qualified surgeons
Qualifications: For thickness mapping and 3D reconstructions, it is specified that "three qualified surgeons" (for anatomical annotations/measurements) or "three experienced otologists" (for Scala Tympany binary masks accuracy review) or "three experienced surgeons" (for cochlear parameter measurements and image fusion landmark points) were used. The document does not specify their years of experience or board certification status, but "qualified" and "experienced" imply relevant expertise in the field of ENT/otology/neurotology.

4. Adjudication Method for the Test Set

The adjudication method appears to be consensus-based or independent review followed by measurement/comparison.

For 3D Reconstruction ground truth, "Three surgeons annotated each CT slice using 3D Slicer" or "annotated the entire inner ear slice by slice" or "Binary masks were generated for each sample and independently reviewed for accuracy by three experienced otologists."
For Thickness Mapping and Cochlear Parameters ground truth, "Thickness manually measured at 5 locations on each CT image by three surgeons" or "The cochlear parameters were manually measured in each ear by three experienced surgeons."
For Image Fusion ground truth, "Cochlear parameters were manually measured by three experienced surgeons. Electrode contact positions were defined, and the software calculated the insertion metrics and frequency allocation" or "3D coordinates of points were manually measured on each post-operative image by 3 experienced surgeons."

While specific "2+1" or "3+1" adjudication systems are not explicitly named, the repeated use of "three surgeons" or "three experienced otologists" suggests an approach where either all three agree, or discrepancies are resolved implicitly or through aggregation (e.g., averaging their measurements). For some metrics (DICE coefficient based on annotations by rater, MAE per rater, Max semitone error per rater), individual rater performance against the algorithm is reported, which could imply each rater's annotation was considered a separate "ground truth" or used to calculate inter-rater variability before comparison with the algorithm. However, for the primary ground truth generation, it seems to involve multiple experts for consistency.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study was not performed. The studies described are validation studies of device performance (algorithm only) against expert-established ground truth, not a comparison of human reader performance with and without AI assistance. The data provided in the tables explicitly show the algorithm's performance (DICE coefficient, MAD, MAE) against the ground truth.

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

Yes, multiple standalone (algorithm only) performance studies were done. The "Automatic Outputs Validation" section (Table 2 and Table 3) explicitly details the performance of the OTOPLAN algorithms for 3D reconstruction, thickness mapping, cochlear parameter calculation, and image fusion against expert-generated ground truth. These tests evaluate the accuracy of the software's automated outputs directly.

7. The Type of Ground Truth Used

The primary type of ground truth used is expert consensus/annotation and manual measurement.

For 3D reconstructions and Scala Tympany, ground truth was established by manual annotation of image slices by three surgeons using 3D Slicer to generate binary masks. For Scala Tympany, these masks were also "independently reviewed for accuracy by three experienced otologists."
For thickness mapping and cochlear parameters, ground truth was established by manual measurements at specified locations or of specific parameters by three experienced surgeons.
For Image Fusion, ground truth involved manual measurement of cochlear parameters and 3D coordinates of landmark points by three experienced surgeons.

8. The Sample Size for the Training Set

The document explicitly states: "Algorithm not trained on a dataset. Use established segmentation methods that don't require training." and "The data from the different sites were pooled based on a prior review to confirm consistency in key image acquisition parameters per validated feature. The data was then separated into a development dataset and validation. The dataset used for algorithm development is entirely separate from the dataset used for performance testing. Prior development, the available data was systematically divided into distinct development and test datasets. At no point was data from the test dataset used during algorithm development."

This indicates that for the "automatic" functions (e.g., 3D reconstruction, thickness mapping, Scala Tympany), Cascination AG claims to either use rule-based/classical image processing methods that do not require machine learning training, or any machine learning component was developed using a "development dataset" that was strictly separate from the "test dataset". The first statement suggests the algorithms may not be deep learning-based, while the second indicates rigorous data separation if they do involve trainable components.

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

Given the statement "Algorithm not trained on a dataset. Use established segmentation methods that don't require training.", it implies there wasn't a "training set" in the traditional machine learning sense that required a separate ground truth establishment process. If there was a "development dataset" used (as implied by the separation statement), the document does not specify how its ground truth was established, only that it was entirely separate from the test set. However, for "established segmentation methods," ground truth implicitly comes from the underlying principles of those methods (e.g., definitions of anatomical structures, physics of image formation).

Summary

FDA 510(k) Clearance Letter - OTOPLAN K242120

Page 1

April 11, 2025

Cascination AG
Gordana Salleles
Head of Regulatory and Clinical Affairs
Steigerhubelstrasse 3
Bern, BE 3008
Switzerland

Re: K242120
Trade/Device Name: Otoplan
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: QQE
Dated: July 19, 2024
Received: July 19, 2024

Dear Ms. Gordana Salleles:

We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

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K242120 – Gordana Salleles Page 2

Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic.

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K242120 – Gordana Salleles Page 3

See the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

Shuchen Peng -S

Shu-Chen Peng, Ph.D.
Assistant Director
DHT1B: Division of Dental and ENT Devices
OHT1: Office of Ophthalmic, Anesthesia,
Respiratory, ENT, and Dental Devices
Office of Product Evaluation and Quality
Center for Devices and Radiological Health

Enclosure

Page 4

DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration

Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.

Indications for Use

Submission Number (if known)
K242120

Device Name
OTOPLAN

Indications for Use (Describe)
OTOPLAN is intended to be used by otologists and neurotologists as a software interface allowing the display, segmentation, and transfer of medical image data from medical CT, MR, and XA imaging systems to investigate anatomy relevant for the preoperative planning and postoperative assessment of otological and neurotological procedures (e.g., cochlear implantation).

Type of Use (Select one or both, as applicable)
☒ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.
DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

Department of Health and Human Services
Food and Drug Administration
Office of Chief Information Officer
Paperwork Reduction Act (PRA) Staff
PRAStaff@fda.hhs.gov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."

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CASCINATION AG

510(K) SUMMARY

K242120

This summary of 510(k) safety and effectiveness information is submitted in accordance with the requirements of 21 CFR §807.92.

I. SUBMITTER

Manufacturer: CASCINATION AG
Steigerhubelstrasse 3
CH-3008 Bern
Switzerland
Tel: +41 31 632 0440
Fax: +41 31 552 04 41

Contact Person: Gordana Salleles
Head of Regulatory and Clinical Affairs

Date Prepared: April 9, 2025

II. SUBJECT DEVICE

Device Name: OTOPLAN
Classification Name: Medical Image Management and Processing System
Regulation: 892.2050
Regulatory Class: Class II
Product Code: QQE

The Subject Device (OTOPLAN version 3.1) is an updated version of the Predicate Device (OTOPLAN version 2.0).

III. PREDICATE DEVICE

Company: CASCINATION AG

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CASCINATION AG

Steigerhubelstrasse 3
CH-3008 Bern
Switzerland

Device name: OTOPLAN
510(k) number: K203486
Product Code: QQE

IV. INDICATIONS FOR USE

V. DEVICE DESCRIPTION

OTOPLAN is a Software as a medical Device (SaMD) which consolidates a DICOM viewer, ruler function, and calculator function into one software platform. The user can

import DICOM-conform medical images, fuse supported images and view these images.
navigate through the images and segment ENT relevant structures (semi-automatic/automatic), which can be highlighted in the 2D images and 3D view.
use a virtual ruler to geometrically measure distances and a calculator to apply established formulae to estimate cochlear length and frequency.
create a virtual trajectory, which can be displayed in the 2D images and 3D view.
identify electrode array contacts, lead, and housing of a cochlear implant to assess electrode insertion and position.
input audiogram-related data that were generated during audiological testing with a standard audiometer and visualize them in OTOPLAN.

OTOPLAN allows the visualization of third-party information, that is, cochlear implant electrodes, implant housings and audio processors.

CASCINATION AG

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CASCINATION AG

VI. SUBSTANTIAL EQUIVALENCE

The following characteristics were compared between the subject device and the predicate device in order to demonstrate substantial equivalence:

Table 1
Summary of the Substantial Equivalence Comparison to Predicate Device

Item	Subject Device (OTOPLAN version 3.1)	Predicate Device (OTOPLAN version 2.0)	Conclusion
Intended Use
Intended Use	Plan surgical procedures in the head and neck area by medical professionals	Plan surgical procedures in the head and neck area by medical professionals	⇨ SameBoth the subject and predicate devices have the same intended use
Indications For Use Statement	OTOPLAN is intended to be used by otologists and neurotologists as a software interface allowing the display, segmentation, and transfer of medical image data from medical CT, MR, and XA imaging systems to investigate anatomy relevant for the preoperative planning and postoperative assessment of otological and neurotological procedures (e.g., cochlear implantation).	OTOPLAN is intended to be used by otologists and neurotologists as a software interface allowing the display, segmentation, and transfer of medical image data from medical CT, MR, and XA imaging systems to investigate anatomy relevant for the preoperative planning and postoperative assessment of otological and neurotological procedures (e.g., cochlear implantation).	⇨ SameBoth the subject and predicate devices have the same indications for use statement.
Technological Characteristics
Type	Standalone Software. Does not control the functions or parameters of any medical device	Standalone Software. Does not control the functions or parameters of any medical device	⇨ Same
Operating System	Windows 10Windows 11	Windows 10	⇨ SameBoth Windows Versions have the same technological characteristics

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CASCINATION AG

Item	Subject Device (OTOPLAN version 3.1)	Predicate Device (OTOPLAN version 2.0)	Conclusion
Functions (Same Functions)	• Data Management• Cochlear Parametrization (based on established formula)• Audiogram• Electrode Visualization• Trajectory Planning• Postoperative Quality Checks• Export Report• 3D reconstruction·Temporal bone·Incus, Malleus·Stapes·Facial nerve·Chorda tympani·External ear canal·Cochlea·Sigmoid sinus·Cochlear bony overhang·Cochlear round window·Electrode contacts	• Data Management• Cochlear Parametrization (based on established formula)• Audiogram• Electrode Visualization• Virtual Trajectory Planning• Postoperative Quality Checks• Export Report• 3D reconstruction·Temporal bone·Incus, Malleus·Stapes·Facial nerve·Chorda tympani·External ear canal·Cochlea·Sigmoid sinus·Cochlear bony overhang·Cochlear round window·Electrode contacts	⇨ Same
Functions (New Functions with Same technological characteristic)	• DICOM Viewer (incl. Fluoroscopy Viewer and plain X-ray)• Electrode contacts (manual identification on plain X-ray; manual and automatic on CT images)• Implant Placement (for visualization only)• Identify the cochlear implant lead and housing	• DICOM Viewer• Electrode contacts (manual and automatic identification on CT images)	⇨ Same Technological characteristics as included in the predicate device

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CASCINATION AG

Item	Subject Device (OTOPLAN version 3.1)	Predicate Device (OTOPLAN version 2.0)	Conclusion
Functions (New functions with different technological characteristic)	• 3D reconstruction·Automatic Temporal bone·Temporal Bone Thickness·Automatic Skin and Skin Thickness·Automatic Inner Ear (cochlea, semicircular canals, internal auditory canal)·Automatic cochlear parameter·Automatic Scala tympani and Scala vestibuli• Image Fusion	---	⇨ Different Technological characteristics which do not affect the safety and effectiveness.
Performance Testing	Software design verification and validation and documentation (the software for this device was considered requiring "Basic Documentation".)Formal Internal Testing StandardsHuman Factors Testing	Software design verification and validation and documentation (the software for this device was considered a "moderate" level of concern.)Formal Internal Testing StandardsHuman Factors Testing	⇨ Same

Substantial Equivalence Discussion

The Subject Device OTOPLAN (version 3.1) is an updated version of the Predicate Device OTOPLAN (version 2.0). Both the Subject Device OTOPLAN (version 3.1) and the Predicate Device OTOPLAN (version 2.0) have the same Intended Use.

The subject device introduces eleven new functions. Four of these functions have the same technological characteristics and seven have different technological characteristics from the predicate device.

The subject device has four new functions with the same technological characteristics:

Fluoroscopy and plain X-Ray Viewer (as part of the DICOM Viewer): simple extension of the DICOM Viewer by standardized DICOM SOP classes
Electrode contacts (manual identification on plain X-ray): similar to the predicate device's contact identification on CT images the subject device allows the user to manual identify electrode contacts on plain X-ray images.

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CASCINATION AG

Implant Placement (for visualization only): overlay function for visualization purposes, similar to other overlay functions in the predicate device
Identify the cochlear implant lead and housing: similar to the predicate device's identification of cochlear implant electrode contacts, the subject device also identifies cochlear implant lead and housing

Those functions have the same technological characteristics as functions in the predicate device. Software verification and validation has been carried out to ensure proper performance of those functions.

Discussion of Technological Differences:

The subject device and predicate device have seven different technological characteristics in the 3D reconstruction and image fusion module. The subject device introduces the following functions:

New 3D reconstructions
- Automatic Temporal Bone: automatic segmentation of the temporal bone in CT images. Formal internal testing using DICE similarity coefficient has been carried out to verify the accuracy of the automatic CT temporal bone 3D reconstruction algorithm. The ground truth has been established by three qualified surgeons.
- Temporal Bone Thickness: calculation of the bone thickness based on temporal bone reconstruction. Formal internal testing: manual measurements at typical areas of interest for ENT surgery by three qualified surgeons were compared to the automated thickness calculation
- Automatic Skin and Skin Thickness: automatic segmentation of the skin in CT images and calculation of the skin thickness based on the skin reconstruction. Formal internal testing using DICE similarity coefficient has been carried out to verify the accuracy of the automatic Skin 3D reconstruction algorithm. The ground truth has been established by three qualified surgeons. Formal internal testing: manual measurements at typical areas of interest for ENT surgery by three qualified surgeons were compared to the automated thickness calculation.
- Automatic Inner Ear: automatic segmentation of the inner ear (cochlea, semicircular canals, internal auditory canal) using a pre-computed statistical shape model (SSM). Formal internal testing using CT and MR images was carried out using DICE similarity coefficient to verify the accuracy of the automatic inner ear reconstruction algorithm. The ground truth has been established by three qualified surgeons.
- Automatic cochlear parameters: based on the Automatic Inner Ear reconstruction this function allows to identify the cochlear landmark points (cochlear diameter (A) and width (B)). Formal internal testing using CT and MR images was carried out comparing the

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CASCINATION AG

automatic parameter calculation to the parameter calculation based on the manual measurements by three qualified surgeons.

Automatic Scala tympani and Scala vestibuli: automatic segmentation of the Scala tympani. Scala tympani: Formal internal testing was carried out using DICE similarity coefficient and other parameters (diameter, width, cross-section, volume) to verify the accuracy of the automatic inner ear reconstruction algorithm, between ground truth and test dataset. The scala vestibuli reconstruction has not been validated and is intended solely for visualization purposes.
Image Fusion: After loading at least two images (CT-CT or CT-MR), two of them can be aligned onto each other). Formal internal testing was carried out by three experienced surgeons marking cochlear landmark points on pre-op (CT) and post-op (CT and MR) images. After the fusion of the pre-op and post-op images the distances between the corresponding landmark points were compared to validate the accuracy of the image fusion function.

VII. PERFORMANCE DATA

The following performance data were provided in support of the substantial equivalence determination.

i. Software Verification and Validation Testing

Software verification and validation testing were conducted to demonstrate safety and effectiveness of the subject device. The testing confirmed that the subject device performs as intended and meets all predefined acceptance criteria. Software validation documentation was prepared according to the "Content of Premarket Submissions for Device Software Functions - Guidance for Industry and Food and Drug Administration Staff" (June 14, 2023). The required documentation level for the subject device has been determined to be "basic documentation level". All planned software verification and validation testing were successfully completed, thereby demonstrating the safety and effectiveness of the subject device.

ii. Automatic Outputs Validation

A summary of the validation of the software's automatic outputs is provided in Table 2 and Table 3.

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CASCINATION AG

Table 2
Summary of Performance Tests – Temporal Bone, Skin, and Inner Ear

Variable	Temporal Bone	Skin	Inner Ear
	Thickness Mapping	3D Reconstruction	Thickness Mapping


Nature of algorithm	Thickness mapping is performed by analyzing spatial relationships within the 3D model to determine the shortest distances between internal and external surfaces.	The algorithm estimates bone structures by analyzing intensity patterns in the imaging data. It then reconstructs a 3D model based on image-derived features.	Thickness mapping is performed by analyzing spatial relationships within the 3D model to determine the shortest distances between internal and external surfaces.
Testing Summary
Aim	Validate the Thickness Measurement	Validate the Reconstruction	Validate the Thickness Measurement
Testing Image Data	○ 43 temporal bones (29 patients)○ 6 different CT, CBCT models○ Slice spacing: 16 different values (mean: 0.45 mm)	○ 31 temporal bones (23 patients)○ 2 different CT, CBCT models○ Slice spacing: 15 different values (mean: 0.34 mm)○ Pixel spacing: 14 values	○ 43 temporal bones (29 patients)○ 6 different CT, CBCT models○ Slice spacing: 16 different values (mean: 0.45 mm)○ Pixel spacing: 19 values

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Variable	Temporal Bone	Skin	Inner Ear
	Thickness Mapping	3D Reconstruction	Thickness Mapping


	○ Pixel spacing: 19 values (mean: 0.27 mm)	(mean: 0.31 mm)	(mean: 0.27 mm)
Image Sites	Pooled (4 clinical sites)	Pooled (4 clinical sites)	Pooled (1 clinical site)
Ground Truth Process	Thickness manually measured at 5 locations on each CT image by three surgeons.	Three surgeons annotated each CT slice using 3D Slicer. For each ear, binary masks were generated per slice.	Thickness manually measured at 5 locations on each CT image by three surgeons.
Test Dataset	Algorithm over the Testing Image Data	Algorithm over the Testing Image Data	Algorithm over the Testing Image Data

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Variable	Temporal Bone	Skin	Inner Ear
	Thickness Mapping	3D Reconstruction	Thickness Mapping


Acceptance Criteria and Test Results	Acceptance Criteria:○ Mean Absolute Difference (MAD) ≤ 0.6 mm○ 95% Confidence Interval (CI) upper limit ≤ 0.8 mm	Acceptance Criteria:○ Mean DICE coefficient ≥ 0.85○ 95% Confidence Interval (CI) lower limit ≥ 0.85	Acceptance Criteria:○ Mean Absolute Difference (MAD) ≤ 0.6 mm○ 95% Confidence Interval (CI) upper limit ≤ 0.8 mm
	Results:○ MAD: 0.17–0.20 mm○ CI: 0.19–0.22	Results:DICE coefficient:○ R1: 0.88 [CI: 0.87–0.89]○ R2: 0.86 [CI: 0.85–0.87]○ R3: 0.89 [CI: 0.88–0.90]	Results:○ MAD: 0.21–0.23 mm○ CI: 0.23–0.26

Note: * Algorithm not trained on a dataset. Use established segmentation methods that don't require training.

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Table 3
Summary of Performance Tests – CT-CT and CT-MR Image Fusion

Variable	Image Fusion
	CT-CT
Nature of Algorithm	A rigid registration algorithm is used to optimize mutual information between two image volumes, yielding a transformation matrix for alignment.
Testing Summary
Aim	Validate Accuracy Using Semitones
Testing Image Data	○ 32 temporal bones (32 patients)○ 2 different CT models○ Pre-op CT:○ Slice spacing: 7 values (mean: 0.41 mm)○ Pixel spacing: 11 values (mean: 0.20 mm)○ Post-op CT:○ Slice spacing: 10 values (mean: 0.50 mm)○ Pixel spacing: 9 values (mean: 0.21 mm)
Ground Truth Process	Cochlear parameters were manually measured by three experienced surgeons. Electrode contact positions were defined, and the software calculated the insertion metrics and frequency allocation.
Image Sites	Pooled (4 clinical sites)
Test Dataset	Algorithm over the Testing Image Data
Acceptance Criteria and Test Results	Acceptance Criteria:○ Maximum mean absolute semitone error per electrode contact < 7.0 semitones

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Variable	Image Fusion
	CT-CT
Results:	Results:Max semitone error (per rater):○ R1: 5.34○ R2: 4.43○ R3: 4.20

Development (Training) and Test Dataset

The data from the different sites were pooled based on a prior review to confirm consistency in key image acquisition parameters per validated feature. The data was then separated into a development dataset and validation. The dataset used for algorithm development is entirely separate from the dataset used for performance testing. Prior development, the available data was systematically divided into distinct development and test datasets. At no point was data from the test dataset used during algorithm development.

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iii. Internal Test Standards

Internal test protocols were executed and documented in test reports to demonstrate performance characteristics of OTOPLAN. This included tests with data sets with known dimensions which were loaded into OTOPLAN and results compared to the know dimension. All test results were in the expected range. The internal tests demonstrate that the subject device can fulfill the expected performance characteristics, and no questions of safety or performance were raised.

iv. Human Factors and Usability Validation

Human factors and usability validation was carried out with the predicate device according to the FDA guidance "Applying Human Factors and Usability Engineering to Medical Devices – Guidance for Industry and Food and Drug Administration Staff (2016-02)" and international standard "AAMI / ANSI / IEC 62366-1:2015, Medical Devices - Part 1: Application of Usability Engineering to Medical Devices". An Impact assessment, comparing the subject device to the predicate device, was carried out to identify hazard-related use scenarios that require additional summative evaluation testing. The additional summative usability testing included 20 and 15 U.S.-based participants from each user group. The additional summative usability testing was successfully completed, demonstrating that the subject device is safe and effective for its intended users, uses, and use environments.

v. Clinical Studies

Clinical testing was not required to demonstrate the safety and effectiveness of OTOPLAN. This conclusion is based upon a comparison of intended use, technological characteristics, and non-clinical performance data (Software Verification and Validation Testing, Human Factors and Usability Validation, and Internal Test Standards).

VIII. CONCLUSIONS

The subject device is substantially equivalent to the predicate device. This conclusion is based upon a comparison of the intended use, technological characteristics, and benchtop testing. An impact assessment on usability, comparing the subject device to the predicate device, and summative usability testing were carried out and demonstrated that the subject device is as safe and effective for the intended users, uses, and use environments as the cited predicate device. Each different technological characteristic has been addressed by a specific performance test. The performance testing demonstrates that the different technological characteristics do not adversely affect the performance of the subject device; therefore, it can be concluded that the subject device is substantially equivalent to the predicate device.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).