(266 days)
Not Found
Yes.
The device description mentions "segment ENT relevant structures (semi-automatic/automatic)" and "The algorithm uses statistical shape modeling techniques combined with image-based feature extraction to identify and segment key inner ear structures from imaging data." The combination of "semi-automatic/automatic" segmentation and "statistical shape modeling techniques combined with image-based feature extraction" implies the use of an AI model for image analysis and segmentation.
No
Explanation: OTOPLAN is a software for displaying, segmenting, and transferring medical image data for preoperative planning and postoperative assessment. It does not directly treat or diagnose a disease or condition, but rather assists medical professionals in their procedures.
Yes
Explanation: The device is described as a "software interface allowing the display, segmentation, and transfer of medical image data...to investigate anatomy relevant for the preoperative planning and postoperative assessment." It helps "identify electrode array contacts, lead, and housing of a cochlear implant to assess electrode insertion and position" and "input audiogram-related data...and visualize them." While it states the information is "solely assistive," its purpose is to process and present medical data to aid in assessments and planning, which are key aspects of diagnosis and treatment planning. The device provides "measurements" and "estimates" of anatomical features and implant positioning which would likely contribute to a physician's overall diagnosis or treatment strategy.
Yes
The device explicitly states "OTOPLAN is a Software as a medical Device (SaMD)". While it processes medical images from CT, MR, and XA systems, and allows for segmentation and measurements, its description focuses entirely on software functions and capabilities. There is no mention of any hardware components being part of the device itself.
No.
The device is a software for medical image processing and analysis. It processes medical images (CT, MR, XA) for diagnostic planning and assessment, not samples from the human body.
No
The letter does not state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. It only mentions the PCCP abbreviation in a general context.
Intended Use / Indications for Use
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).
Product codes (comma separated list FDA assigned to the subject device)
QQE
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
CT, MR, XA, plain X-ray, CBCT
Anatomical Site
Ear, head and neck area, inner ear, temporal bone, skin, cochlea, semicircular canals, internal auditory canal, Scala tympani, Scala vestibuli
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Otologists and neurotologists / Not Found
Description of the training set, sample size, data source, and annotation protocol
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. 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.
Description of the test set, sample size, data source, and annotation protocol
Temporal Bone Thickness Mapping:
- Sample Size: 43 temporal bones (29 patients)
- Data Source: 6 different CT, CBCT models from 4 clinical sites
- Annotation Protocol: Thickness manually measured at 5 locations on each CT image by three surgeons.
Temporal Bone 3D Reconstruction:
- Sample Size: 31 temporal bones (23 patients)
- Data Source: 2 different CT, CBCT models from 4 clinical sites
- Annotation Protocol: Three surgeons annotated each CT slice using 3D Slicer. For each ear, binary masks were generated per slice.
Skin Thickness Mapping:
- Sample Size: 43 temporal bones (29 patients)
- Data Source: 6 different CT, CBCT models from 1 clinical site
- Annotation Protocol: Thickness manually measured at 5 locations on each CT image by three surgeons.
Skin 3D Reconstruction:
- Sample Size: 31 temporal bones (23 patients)
- Data Source: 2 different CT, CBCT models from 4 clinical sites
- Annotation Protocol: Three surgeons annotated each CT slice using 3D Slicer. For each ear, binary masks were generated per slice.
Scala Tympany:
- Sample Size: 450 clinical-resolution CBCT datasets derived from 75 cochleae
- Data Source: Not specified, with ground truth data from Synchrotron Radiation Phase-Contrast Imaging (SR-PCI) with 9 µm resolution derived from same 75 cochleae.
- Annotation Protocol: Binary segmentation masks were created for each sample and independently reviewed for accuracy by three experienced otologists.
Inner Ear (Cochlea, Semi-circular canals, internal auditory canal) 3D Reconstruction CT:
- Sample Size: 44 ears (27 patients)
- Data Source: 2 different CT models from 4 clinical sites
- Annotation Protocol: Three surgeons annotated the entire inner ear slice by slice using 3D Slicer, generating binary masks for each slice per ear.
Inner Ear (Cochlea, Semi-circular canals, internal auditory canal) 3D Reconstruction MR:
- Sample Size: 41 ears (24 patients)
- Data Source: 4 different MR models from 4 clinical sites
- Annotation Protocol: The inner ear was annotated slice by slice by three surgeons using 3D Slicer, with binary masks exported for each slice per ear.
Cochlear Parameters CT:
- Sample Size: 61 ears (53 patients)
- Data Source: 4 different CT, CBCT models from 4 clinical sites
- Annotation Protocol: The cochlear parameters were manually measured in each ear by three experienced surgeons.
Cochlear Parameters MR:
- Sample Size: 63 ears (52 patients)
- Data Source: 5 different MR models from 4 clinical sites
- Annotation Protocol: The cochlear parameters were manually measured in each ear by three experienced surgeons.
CT-CT Image Fusion - Accuracy Using Semitones:
- Sample Size: 32 temporal bones (32 patients)
- Data Source: 2 different CT models from 4 clinical sites
- Annotation Protocol: Cochlear parameters were manually measured by three experienced surgeons. Electrode contact positions were defined, and the software calculated the insertion metrics and frequency allocation.
CT-CT Image Fusion - Accuracy Using Landmark Point Distances:
- Sample Size: 32 temporal bones (32 patients)
- Data Source: 2 different CT models from 4 clinical sites
- Annotation Protocol: 3D coordinates of points were manually measured on each post-operative image by 3 experienced surgeons.
CT-MR Image Fusion - Accuracy Using Semitones:
- Sample Size: 31 temporal bones (25 patients)
- Data Source: CT: 4 models, MR: 4 models from 4 clinical sites
- Annotation Protocol: Cochlear parameters were manually measured by three experienced surgeons. Electrode contact positions were defined, and the software calculated the insertion metrics and frequency allocation.
CT-MR Image Fusion - Accuracy Using Landmark Point Distances:
- Sample Size: 31 temporal bones (25 patients)
- Data Source: CT: 4 models, MR: 4 models from 4 clinical sites
- Annotation Protocol: 3D coordinates of points were manually measured on each post-operative image by 3 experienced surgeons.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Study Type: Software Verification and Validation Testing, Automatic Outputs Validation, Internal Test Standards, Human Factors and Usability Validation. Clinical Studies were not required.
Software Verification and Validation Testing:
- Study Type: Software verification and validation testing
- Sample Size: Not explicitly stated, but performed over the entire software.
- Key Results: Confirmed that the subject device performs as intended and meets all predefined acceptance criteria. All planned software verification and validation testing were successfully completed, demonstrating the safety and effectiveness.
Automatic Outputs Validation:
- Study Type: Performance Tests for Temporal Bone, Skin, Inner Ear, and Image Fusion.
- Key Results (Temporal Bone Thickness Mapping):
- Mean Absolute Difference (MAD): 0.17–0.20 mm
- 95% Confidence Interval (CI) upper limit: 0.19–0.22 mm
- Met acceptance criteria (MAD ≤ 0.6 mm, CI upper limit ≤ 0.8 mm).
- Key Results (Temporal Bone 3D Reconstruction):
- 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]
- Met acceptance criteria (Mean DICE coefficient ≥ 0.85, CI lower limit ≥ 0.85).
- Key Results (Skin Thickness Mapping):
- MAD: 0.21–0.23 mm
- CI: 0.23–0.26 mm
- Met acceptance criteria (MAD ≤ 0.6 mm, CI upper limit ≤ 0.8 mm).
- Key Results (Skin 3D Reconstruction):
- DICE coefficient: R1: 0.89 [CI: 0.88–0.90], R2: 0.87 [CI: 0.86–0.88], R3: 0.86 [CI: 0.84–0.88]
- Met acceptance criteria (Mean DICE coefficient ≥ 0.68, CI lower limit ≥ 0.68).
- Key Results (Scala Tympany):
- DICE coefficient: 0.76 [CI: 0.75–0.77]
- Met acceptance criteria (Mean DICE coefficient ≥ 0.65, CI lower limit ≥ 0.65).
- Key Results (Cochlea, Semi-circular canals, internal auditory canal 3D Reconstruction - CT):
- DICE coefficient: R1: 0.82 [CI: 0.81–0.83], R2: 0.84 [CI: 0.83–0.85], R3: 0.85 [CI: 0.84–0.86]
- Met acceptance criteria (Mean DICE coefficient ≥ 0.80, CI lower limit ≥ 0.80).
- Key Results (Cochlea, Semi-circular canals, internal auditory canal 3D Reconstruction - MR):
- DICE coefficient: R1: 0.81 [CI: 0.80–0.82], R2: 0.83 [CI: 0.82–0.84], R3: 0.84 [CI: 0.83–0.85]
- Met acceptance criteria (Mean DICE coefficient ≥ 0.80, CI lower limit ≥ 0.80).
- Key Results (Cochlear Parameters - CT):
- Mean absolute error (MAE) for CDLoc: R1: 0.59 ± 0.37 mm, R2: 0.64 ± 0.44 mm, R3: 0.62 ± 0.39 mm
- Met acceptance criteria (MAE CDLoc measurement ≤ 1.5 mm).
- Key Results (Cochlear Parameters - MR):
- MAE (±SD) for CDLoc: R1: 0.56 ± 0.42 mm, R2: 0.70 ± 0.39 mm, R3: 0.64 ± 0.43 mm
- Met acceptance criteria (MAE CDLoc measurement ≤ 1.5 mm).
- Key Results (CT-CT Image Fusion - Accuracy Using Semitones):
- Max semitone error (per rater): R1: 5.34, R2: 4.43, R3: 4.20
- Met acceptance criteria (Maximum mean absolute semitone error per electrode contact
§ 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).
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."
Page 5
CASCINATION AG
510(K) SUMMARY
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
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).
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.
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.
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 | ⇨ Same |
| Both 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). | ⇨ Same |
| Both 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 10 | ||
| Windows 11 | Windows 10 | ⇨ Same | |
| Both 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 Standards | |||
| Human Factors Testing | Software design verification and validation and documentation (the software for this device was considered a "moderate" level of concern.) | ||
| Formal Internal Testing Standards | |||
| Human 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.
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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.
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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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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 | ○ 31 temporal bones (23 patients) | ||
| ○ 2 different CT, CBCT models | |||
| ○ Slice spacing: 15 different values (mean: 0.34 mm) | |||
| ○ Pixel spacing: 14 values | ○ Test data: 450 clinical-resolution CBCT datasets derived from 75 cochleae with voxel sizes 0.1 mm to 0.6 mm and pixel spacing 0.1 mm to 0.6 mm | ||
| ○ Ground truth data from Synchrotron Radiation Phase- | ○ 44 ears (27 patients) | ||
| ○ 2 different CT models | |||
| ○ Slice spacing: 7 different values (mean: 0.42 mm) | |||
| ○ Pixel spacing: 16 | ○ 41 ears (24 patients) | ||
| ○ 4 different MR models | |||
| ○ Slice spacing: 7 different values (mean: 0.53 mm) | |||
| ○ Pixel spacing: 12 | ○ 61 ears (53 patients) | ||
| ○ 4 different CT, CBCT models | |||
| ○ Slice spacing: 10 different values (mean: 0.55 mm) | |||
| ○ Pixel spacing: 24 values (mean: 0.37 mm) | ○ 63 ears (52 patients) | ||
| ○ 5 different MR models | |||
| ○ Slice spacing: 7 different values (mean: 0.52 mm) | |||
| ○ Pixel spacing: 15 values (mean: 0.44 mm) |
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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 | Acceptance Criteria: | ||
| ○ Mean DICE coefficient ≥ 0.68 | |||
| ○ 95% Confidence Interval (CI) lower limit ≥ 0.68 | Acceptance Criteria: | ||
| ○ Mean DICE coefficient ≥ 0.65 | |||
| ○ 95% CI lower limit ≥ 0.65 | Acceptance Criteria: | ||
| ○ Mean DICE coefficient ≥ 0.80 | |||
| ○ 95% Confidence Interval (CI) lower limit ≥ 0.80 | Acceptance Criteria: | ||
| ○ Mean DICE coefficient ≥ 0.80 | |||
| ○ 95% Confidence Interval (CI) lower limit ≥ 0.80 | Acceptance Criteria: | ||
| ○ Mean absolute error (MAE) CDLoc measurement ≤ 1.5 mm | Acceptance Criteria: | ||
| ○ Mean absolute error (MAE) CDLoc measurement ≤ 1.5 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 | Results: | ||
| DICE coefficient: | |||
| ○ R1: 0.89 [CI: 0.88–0.90] | |||
| ○ R2: 0.87 [CI: 0.86–0.88] | |||
| ○ R3: 0.86 [CI: 0.84–0.88] | Results: | ||
| DICE coefficient: 0.76 [CI: 0.75–0.77] | Results: | ||
| DICE coefficient: | |||
| ○ R1: 0.82 [CI: 0.81–0.83] | |||
| ○ R2: 0.84 [CI: 0.83–0.85] | |||
| ○ R3: 0.85 [CI: 0.84–0.86] | Results: | ||
| DICE coefficient: | |||
| ○ R1: 0.81 [CI: 0.80–0.82] | |||
| ○ R2: 0.83 [CI: 0.82–0.84] | |||
| ○ R3: 0.84 [CI: 0.83–0.85] | Results: | ||
| MAE (±SD) for CDLoc: | |||
| ○ R1: 0.59 ± 0.37 mm | |||
| ○ R2: 0.64 ± 0.44 mm | |||
| ○ R3: 0.62 ± 0.39 mm | Results: | ||
| MAE (±SD) for CDLoc: | |||
| ○ R1: 0.56 ± 0.42 mm | |||
| ○ R2: 0.70 ± 0.39 mm | |||
| ○ R3: 0.64 ± 0.43 mm |
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) | ○ 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) | ○ 31 temporal bones (25 patients) |
| ○ CT: 4 models | |
| ○ Slice spacing: 12 values (mean: 0.65 mm) | |
| ○ Pixel spacing: 17 values (mean: 0.21 mm) | |
| ○ MR: 4 models | |
| ○ Slice spacing: 9 values (mean: 0.52 mm) | |
| ○ Pixel spacing: 11 values (mean: 0.33 mm) | ○ 31 temporal bones (25 patients) |
| ○ CT: 4 models | |
| ○ Slice spacing: 12 values (mean: 0.65 mm) | |
| ○ Pixel spacing: 17 values (mean: 0.21 mm) | |
| ○ MR: 4 models | |
| ○ Slice spacing: 9 values (mean: 0.52 mm) | |
| Pixel spacing: 11 values (mean: 0.33 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 |