(294 days)
Axial3DInsight is intended for use as a cloud-based service and image segmentation framework for the transfer of DICOM imaging information from a medical scanner to an output file.
The Axial3DInsight output file can be used for the fabrication of the output file using additive manufacturing methods.
The output file or physical replica can be used for treatment planning.
The output file or physical replica can be used for diagnostic purposes in the field of orthopedic trauma, orthopedic, maxillofacial, and cardiovascular applications.
Axial3DInsight should be used with other diagnostic tools and expert clinical judgment.
Axial3D Insight is a secure, highly available cloud-based image processing, segmentation and 3D modelling framework for the transfer of imaging information either as a 3D printed physical model.
The acceptance criteria and the study proving the device meets them are described below, based on the provided text.
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state a table of acceptance criteria with specific quantitative metrics. However, it describes two validation studies and their outcomes, implying that meeting these outcomes constituted the acceptance.
Inferred Acceptance Criteria & Reported Performance:
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Clinical Segmentation Performance: Consistent and diagnostically acceptable segmentation by radiologists. | Clinical Segmentation Performance Study: "The Clinical Segmentation Performance study was conducted with 3 radiologists reviewing the segmentation of 12 cases across the fields of orthopedics, trauma, maxillofacial and cardiovascular. Axial3D adopted a peer reviewed medical imaging review framework of RADPEER to capture the assessment and feedback from the radiologists involved – all cases were scored within the acceptance criteria of 1 or 2a [1]." (This indicates successful segmentation as per expert review). |
| Intended Use Validation (3D Models): 3D models produced by the device satisfy end-user needs and indications for use. | Intended Use Validation Study: "The Intended Use validation study of the device was conducted with 9 physicians reviewing 12 cases across the fields of Orthopedics, Trauma, Maxillofacial, and Cardiovascular, as defined in the Intended Use statement of the device. This study concluded successful validation of the 3D models produced by Axial3D demonstrating the device outputs satisfied end user needs and indications for use." |
| Software Verification & Validation: All software requirements and risk analysis successfully verified and traced. | "Axial3D has conducted software verification and validation, in accordance with the FDA quidance, General Principles of Software Validation; Final Guidance for Industry and FDA Staff, issued on January 11, 2002. All software requirements and risk analysis have been successfully verified and traced." |
| Machine Learning Model Validation: Independent verification and validation of machine learning models before inclusion. | "Axial™- machine learning models were independently verified and validated before inclusion in the Axial3D Insight device." (Detailed data on number of images, slice spacing, and pixel size used for validation of Cardiac CT/CTa, Neuro CT/CTa, Ortho CT, and Trauma CT models are provided in Table 5-4, indicating the scope of this validation). |
2. Sample Sizes and Data Provenance
-
Test Set Sample Sizes:
- Clinical Segmentation Performance Study: 12 cases
- Intended Use Validation Study: 12 cases
- Machine Learning Model Validation:
- Cardiac CT/CTa: 4,838 images
- Neuro CT/CTa: 4,041 images
- Ortho CT: 10,857 images
- Trauma CT: 19,134 images
-
Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It only mentions the imaging scanner manufacturers and models used in the validation datasets: GE Medical Systems, Siemens, Phillips, and Toshiba.
3. Number of Experts and Qualifications
- Clinical Segmentation Performance Study: 3 radiologists. No specific years of experience are mentioned, but they are described as "radiologists."
- Intended Use Validation Study: 9 physicians. No specific qualifications (e.g., orthopedic surgeon, maxillofacial surgeon, cardiologist) or years of experience are mentioned, only "physicians."
4. Adjudication Method
- For the Clinical Segmentation Performance Study, the "RADPEER" framework was adopted. All cases were scored within the acceptance criteria of 1 or 2a. While RADPEER is a peer review system, the specific adjudication
method for discrepancies among the 3 radiologists (e.g., majority vote, consensus meeting, 2+1, 3+1) is not explicitly detailed. It only states that all cases met the acceptance criteria, suggesting agreement or successful resolution. - For the Intended Use Validation Study, no adjudication method is explicitly described beyond "9 physicians reviewing 12 cases" and concluding "successful validation."
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No, a MRMC comparative effectiveness study was not explicitly mentioned as being done to evaluate how much human readers improve with AI vs. without AI assistance. The studies described focus on validation of the device's output and the AI models, rather than human-in-the-loop performance improvement. The text mentions that the Axial™ machine learning models are used to generate an initial segmentation, but the final segmentation and validation are done by "Axial3D trained staff," implying a human-in-the-loop process, but no comparative study to measure effect size is presented in this document.
6. Standalone (Algorithm Only) Performance
- Yes, standalone performance of the machine learning models was conducted. The document states: "Axial™- machine learning models were independently verified and validated before inclusion in the Axial3D Insight device." Table 5-4 provides the number of images used for validation for different clinical areas (Cardiac, Neuro, Ortho, Trauma CT), indicating a quantitative assessment of the models themselves. However, the specific metrics (e.g., Dice score, sensitivity, specificity) for this standalone performance are not provided in the text.
7. Type of Ground Truth Used
- For the Clinical Segmentation Performance Study: The ground truth was established by the consensus or review of the 3 radiologists, consistent with a form of expert consensus.
- For the Intended Use Validation Study: The ground truth was based on the expert clinical judgment of the 9 physicians, who reviewed the 3D models and concluded their utility for intended use.
- For the Machine Learning Model Validation: The document states that "The Axial™- machine learning model training data used during the algorithm development was explicitly kept separate and independent from the validation data used." While it doesn't explicitly state the type of ground truth for this segment, it can be inferred that the ground truth for the validation of the machine learning models was also based on expert-derived segmentations used to compare against the model's output.
8. Sample Size for the Training Set
- The document states: "The Axial™- machine learning model training data used during the algorithm development was explicitly kept separate and independent from the validation data used." However, the sample size for the training set is not provided. Only the sample sizes for the validation data are listed (Table 5-4).
9. How Ground Truth for Training Set was Established
- The document does not explicitly describe how the ground truth for the training set was established. It only implies that training data was distinct from validation data. Given the nature of medical image segmentation, it is highly probable that the ground truth for the training set was established through manual segmentation by human experts (e.g., radiologists, clinical experts), but this is an inference and not explicitly stated in the provided text.
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Image /page/0/Picture/0 description: The image contains two logos. On the left is the Department of Health & Human Services logo. On the right is the FDA logo, which includes the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
Axial Medical Printing Limited % Sujith Shetty Executive Vice President Maxis Medical LLC 7052 Hollow Lake Way SAN JOSE, CALIFORNIA 95120
Re: K222745
Trade/Device Name: Axial3D Insight Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: OIH Dated: June 2, 2023 Received: June 2, 2023
Dear Sujith Shetty:
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 (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 located 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.
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
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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 of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-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 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/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. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/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,
Wenbo Li
for Jessica Lamb
Jessica Lamb, Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
510(k) Number (if known) K222745
Device Name Axial3DInsight
Indications for Use (Describe)
Axial3DInsight is intended for use as a cloud-based service and image segmentation framework for the transfer of DICOM imaging information from a medical scanner to an output file.
The Axial3DInsight output file can be used for the fabrication of the output file using additive manufacturing methods.
The output file or physical replica can be used for treatment planning.
The output file or physical replica can be used for diagnostic purposes in the field of orthopedic trauma, orthopedic, maxillofacial, and cardiovascular applications.
Axial3DInsight should be used with other diagnostic tools and expert clinical judgment.
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) |
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Image /page/3/Picture/1 description: The image shows the logo for Axial3D. The logo consists of the word "axial" in a dark gray sans-serif font, with a small teal diamond above the "i". To the right of "axial" is the superscript "3D". Below the word "axial" is the tagline "Patient data made real" in a teal sans-serif font.
5 510(k) Summary
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Image /page/4/Picture/0 description: The image shows the logo for Axial3D. The logo consists of the word "axial" in a dark blue sans-serif font, with a small teal diamond above the "i". To the right of "axial" is the superscript "3D". Below the word "axial" is the tagline "Patient data made real" in a teal sans-serif font.
5.1 510(k) Summary
This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of SMDA 1990 and 21 CRF 807.92.
510 (k) number: K222745
5.2 Applicant Information
Axial Medical Printing Limited
17A Ormeau Avenue
Belfast
BT2 8HD
United Kingdom
Tel: +44 (0)28 90183590
Contact Person 5.2.1
Dr. Sujith Shetty, Executive Vice President, Maxis Medical, Consultant
Email: sjshetty@maxismedical.com
Phone: 1-408-887-3211
5.3 Device Information
| Trade Name | Axial3D Insight |
|---|---|
| Common Name | Automated Radiological Image Processing Software |
| Regulation number | 892.2050 |
| Regulation Name | Medical Image Management and Processing System |
| Regulatory Class | II |
| Product Code | QIH |
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5.4 Predicate Device
| Table 5-1 – Predicate Device | ||
|---|---|---|
| ------------------------------ | -- | -- |
| Name | Manufacturer | 510(k)# |
|---|---|---|
| Axial3D Cloud Segmentation Service | Axial Medical Printing Limited | K221511 |
This predicate has not been subject to a design-related recall. No reference devices were used in this submission.
Device Description 5.5
Axial3D Insight is a secure, highly available cloud-based image processing, segmentation and 3D modelling framework for the transfer of imaging information either as a 3D printed physical model.
5.5.1 Indications for Use
Axial3D Insight is intended for use as a cloud-based service and image segmentation framework for the transfer of DICOM imaging information from a medical scanner to an output file.
The Axial3D Insight output file can be used for the fabrication of physical replicas of the output file using additive manufacturing methods.
The output file or physical replica can be used for treatment planning.
The output file or the physical replica can be used for diagnostic purposes in the field of orthopedic trauma, orthopedic, maxillofacial, and cardiovascular applications.
Axial3D Insight should be used with other diagnostic tools and expert clinical judgment.
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Image /page/6/Picture/1 description: The image shows the logo for Axial3D. The word "axial" is written in a dark blue color, with a small teal diamond above the "i". The "3D" is written in a smaller font and is located to the upper right of the word "axial". Below the word "axial", the phrase "Patient data made real" is written in a teal color.
Comparison of Intended Use to Predicate and Reference Devices 5.6
| Attribute | Axial3D Insight(Proposed Device) | Axial3D CloudSegmentationService(PredicateDevice) | Mimics InPrint(ReferenceDevice) | Comparison |
|---|---|---|---|---|
| Device Manufacturer | Axial MedicalPrinting Limited | Axial MedicalPrinting Limited | Materialise N.V. | N/A |
| Device Name | Axial3D Insight | Axial3D Insight | Mimics inPrint | N/A |
| Device Tradeor ProprietaryName | Axial3D Insight | Axial3D Insight | Mimics inPrint | N/A |
| 510(k)Number | TBD | K221511 | K173619 | N/A |
| DeviceRegulationName: | AutomatedRadiological ImageProcessing Software | Medical imagemanagement andprocessingsystem | Picture archivingandcommunicationssystem | Different -Updated basedon additionalprocessing |
| DeviceRegulationNumber: | 21 CFR 892.2050 | 21 CFR 892.2050 | 21 CFR 892.2050 | Equivalent |
| DeviceProductCode: | QIH | LLZ | LLZ | Different -Updated basedon additionalprocessing |
| DeviceClassificationFDA: | Class II | Class II | Class II | Equivalent |
| Indication forUse | Axial3D Insight isintended for use as acloud-based serviceand imagesegmentationframework for thetransfer of DICOMimaging informationfrom a medicalscanner to an outputfile. | Axial3D CloudSegmentationService isintended for useas a cloud basedservice andimagesegmentationsystem for thetransfer ofDICOM imaging | Mimics inPrint isintended for useas a softwareinterface andimagesegmentationsystem for thetransfer ofDICOM imaginginformation froma medical | Equivalent |
| Attribute | Axial3D Insight(Proposed Device) | Axial3D CloudSegmentationService(PredicateDevice) | Mimics InPrint(ReferenceDevice) | Comparison |
| The Axial3D Insightoutput file can beused for thefabrication ofphysical replicas ofthe output file usingadditivemanufacturingmethods.The output file orphysical replica canbe used for treatmentplanning.The output file orphysical replica canbe used fordiagnostic purposesin the field of trauma,orthopedic,maxillofacial, andcardiovascularapplications.Axial3D Insightshould be used withother diagnostic toolsand expert clinicaljudgment. | information froma medicalscanner to anoutput file.The Axial3DCloudSegmentationService outputfile can be usedfor the fabricationof physicalreplicas of theoutput file usingadditivemanufacturingmethods.The output file orphysical replicacan be used fortreatmentplanning.The physicalreplica can beused fordiagnosticpurposes in thefield oforthopedic,maxillofacial andcardiovascularapplications.Axial3D CloudSegmentationService shouldbe used inconjunction withother diagnostictools and expertclinical judgment. | scanner to anoutput file. It isalso used as pre-operativesoftware fortreatmentplanning. For thispurpose, theMimics inPrintoutput file can beused for thefabrication ofphysical replicasof the output fileusing traditionalor additivemanufacturingmethods.The physicalreplica can beused fordiagnosticpurposes in thefield oforthopedic,maxillofacial, andcardiovascularapplications.Mimics inPrintcan be used fordiagnosticpurposes in thefield oforthopedic,maxillofacial, andcardiovascularapplications.Mimics inPrintshould be used inconjunction with | ||
| Attribute | Axial3D Insight(Proposed Device) | Axial3D CloudSegmentationService(PredicateDevice) | Mimics InPrint(ReferenceDevice) | Comparison |
| Intended Use | Axial MedicalPrinting Limiteds,Axial3D Insightprovides patient-specific 1:1 scalereplica models, eitheras a digital file or asa 3D printed physicalmodel.The digital file or 3Dprinted physicalmodel is intended tobe used inconjunction with theDICOM images andexpert clinicaljudgement. Theapplications for usingthe physical 3Dprinted physicalmodel as apresurgical planningtool are as follows:Preoperativeplanning of surgicaltreatment optionsincluding planning forsurgical instruments,aiding decisions onimplants, and aidingthe surgicaltreatment plan., Allplanning using the3D replica modelshould be carried out | Axial3D CloudSegmentationService isintended for useas a cloud basedservice andimagesegmentationsystem for thetransfer ofDICOM imaginginformation froma medicalscanner to anoutput file.The Axial3DCloudSegmentationService outputfile can be usedfor the fabricationof physicalreplicas of theoutput file usingadditivemanufacturingmethods.The output file orphysical replicacan be used fortreatmentplanning.The physicalreplica can beused for | other diagnostictools and expertclinicaljudgement.Mimics InPrint isintended for useas a softwareinterface andimagesegmentationsystem for thetransfer ofDICOM imaginginformation froma medicalscanner to anoutput file. It isalso used as pre-operativesoftware fortreatmentplanning. For thispurpose, theMimics InPrintoutput file can beused for thefabrication ofphysical replicasof the output fileusing traditionalor additivemanufacturingmethods. Thephysical replicacan be used fordiagnosticpurposes in thefield oforthopedic,maxillofacial and | Similar |
| Attribute | Axial3D Insight(Proposed Device) | Axial3D CloudSegmentationService(PredicateDevice) | Mimics InPrint(ReferenceDevice) | Comparison |
| with the assistanceof the DICOMimagesCommunication withthe surgical team todiscuss the surgicaltreatment plan inconjunction withDICOM imagesCommunication withthe patient to discussthe surgicaltreatment plan inconjunction withDICOM imagesEducation tool forsurgical planning.The 3D printedphysical model canbe used for surgicalplanning in thefollowingapplications:orthopedics andtrauma, maxillofacial,and cardiovascularsurgery. | diagnosticpurposes in thefield oforthopedic,maxillofacial andcardiovascularapplications.Axial3D CloudSegmentationService shouldbe used inconjunction withother diagnostictools and expertclinical judgment. | cardiovascularapplications.Mimics inPrintshould be used inconjunction withother diagnostictools and expertclinicaljudgement. | ||
| Method ofUse | Used in conjunctionwith other diagnostictools and expertclinical judgement. | Used inconjunction withother diagnostictools and expertclinicaljudgement. | Used inconjunction withother diagnostictools and expertclinicaljudgement. | Equivalent |
| Environment | Hospital | Hospital | Hospital | Equivalent |
| OTC orPrescriptionDevice | Prescription Use | Prescription Use | Prescription Use | Equivalent |
| Attribute | Axial3D Insight(Proposed Device) | Axial3D CloudSegmentationService(PredicateDevice) | Mimics InPrint(ReferenceDevice) | Comparison |
| Level ofConcern | Moderate | Moderate | Moderate | Equivalent |
| V&V | Complies with FDAGuidanceRequirement | Complies withFDA GuidanceRequirement | Complies withFDA GuidanceRequirement | Equivalent |
Table 5-2 – Predicate Device Comparison: Intended Use
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Image /page/7/Picture/1 description: The image shows the logo for Axial3D. The logo consists of the word "axial" in a dark blue sans-serif font, with a small teal diamond above the "i". To the right of "axial" is the number "3D" in a smaller, lighter font. Below the logo is the tagline "Patient data made real" in a teal sans-serif font.
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Image /page/8/Picture/0 description: The image shows the logo for Axial3D. The logo consists of the word "axial" in a dark blue sans-serif font, with a small teal diamond above the "i". To the right of "axial" is the superscript "3D". Below the word "axial" is the tagline "Patient data made real" in a teal sans-serif font.
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Image /page/9/Picture/1 description: The image contains the logo for Axial3D. The logo consists of the word "axial" in a dark blue sans-serif font, with a small teal diamond shape above the "i". To the right of "axial" is the number "3D" in a smaller font size. Below the logo is the tagline "Patient data made real" in a teal sans-serif font.
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Image /page/10/Picture/1 description: The image shows the logo for Axial3D. The word "axial" is in bold, dark blue font, and the "3D" is in a smaller, lighter blue font. Below the logo, the words "Patient data made real" are written in a light blue font.
5.7 Comparison of Technological Characteristics to the Predicate Device and Reference Device
| Attribute | Axial3D Insight(ProposedDevice) | Axial3D CloudSegmentationService(PredicateDevice) | Mimics InPrint(ReferenceDevice) | Comparison |
|---|---|---|---|---|
| Method of Use | software interface | software interface | software interface | Equivalent |
| ComputerPlatform andOperatingSystem | Microsoft Edge(v104), Safari (v15)or Chrome (v103)or equivalent | Internet Explorer 11or equivalent | Windows 7 — 64bitInternet Explorer 8and compatibleIntel Core 2 Duo /AMD X2 AM2 orequivalent | The proposeddevice onlyrequiresMicrosoftEdge (v104),Safari (v15)or Chrome(v103) orequivalentto operate.Underlyinghardware isirrelevant tothe user as itis hosted byAxial Medical.The changedoes notaffectestablishing |
| Attribute | Axial3D Insight(ProposedDevice) | Axial3D CloudSegmentationService(PredicateDevice) | Mimics InPrint(ReferenceDevice) | Comparison |
| substantialequivalenceas the outputof theproposeddevice andpredicatedevices isequivalent. | ||||
| SupportedModalities | CT and CTA | CT | CT, MRI, X-ray | The proposeddevice uses asubset of thepredicatedevice imagemodalities |
| Imageregistration | Yes | Yes | Yes | Equivalent |
| SegmentationFeatures | A combination ofautomated toolswith smart editingtools | A combination ofautomated toolswith smart editingtools | Combination ofautomated toolswith smart editingtools | Equivalent |
| ViewManipulationand VolumeRendering | Yes | Yes | Yes | Equivalent |
| Regions andVolumes ofInterest (ROI) | Orthopedics /TraumaCardiovascularCranio-Maxillofacial | Orthopedics /TraumaCardiovascularCranio-Maxillofacial | Orthopedics /TraumaCardiovascularCranio-Maxillofacial | Equivalent |
| Region/volumeof interestmeasurementsand sizemeasurements | Yes | Yes | Yes | Equivalent |
| Attribute | Axial3D Insight(ProposedDevice) | Axial3D CloudSegmentationService(PredicateDevice) | Mimics InPrint(ReferenceDevice) | Comparison |
| Region/VolumeQuantification | Yes | Yes | Yes | Equivalent |
Table 5-3 – Predicate Comparison: Technology
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Image /page/11/Picture/1 description: The image shows the logo for Axial3D. The word "axial" is in a bold, dark blue font, with a small teal diamond above the "i". To the right of "axial" is a smaller "3D" in the same dark blue color. Below the logo is the tagline "Patient data made real" in a teal color.
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Image /page/12/Picture/1 description: The image shows the logo for Axial3D. The word "axial" is in a dark blue color, and the "3D" is in a smaller font and is located to the upper right of the word "axial". Above the "i" in axial is a teal diamond shape. Below the logo is the phrase "Patient data made real" in a teal color.
5.8 Performance Data
5.8.1 Axial3D Insight Device Validation
Axial3D performed software design verification and validation testing on all three software components of the device and the software documentation for a Moderate Level of Concern software in accordance with the FDA Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices May 11, 2015.
Axial3D has conducted software verification and validation, in accordance with the FDA quidance, General Principles of Software Validation; Final Guidance for Industry and FDA Staff, issued on January 11, 2002. All software requirements and risk analysis have been successfully verified and traced.
In addition to the human factors validation of the Axial3D Insight device. Axial3D conducted two validation studies - the Clinical Segmentation performance and the Intended Use of the device output - the 3D patient specific model.
The Clinical Segmentation Performance study was conducted with 3 radiologists reviewing the segmentation of 12 cases across the fields of orthopedics, trauma, maxillofacial and cardiovascular. Axial3D adopted a peer reviewed medical imaging review framework of RADPEER to capture the assessment and feedback from the radiologists involved - all cases were scored within the acceptance criteria of 1 or 2a [1].
The Intended Use validation study of the device was conducted with 9 physicians reviewing 12 cases across the fields of Orthopedics, Trauma, Maxillofacial, and Cardiovascular, as defined in the Intended Use statement of the device. This study concluded successful validation of the 3D models produced by Axial3D demonstrating the device outputs satisfied end user needs and indications for use.
Axial™- Machine Learning Validation 5.8.2
AxialM- machine learning models are used to generate an initial segmentation of cases, however the output of these models is not used in isolation to produce the final 3D patient specific model. The segmentations produced by the Axial™ machine learning models are used by Axial3D trained
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Image /page/13/Picture/1 description: The image shows the logo for Axial3D. The word "axial" is in a dark blue color, with a teal diamond above the "i". To the right of "axial" is the number "3D" in a smaller font. Below the logo is the phrase "Patient data made real" in teal.
staff who complete the final segmentation and validation of the quality of each 3D patient specific model produced.
Axial™- machine learning models were independently verified and validated before inclusion in the Axial3D Insight device. Details of the data used in the validation of each machine learning model is provided below.
| CardiacCT/CTa | NeuroCT/CTa* | Ortho CT | Trauma CT | |
|---|---|---|---|---|
| Number of ImagesUsed for Validation | 4,838 | 4,041 | 10,857 | 19,134 |
| Slice Spacing Range(Min, Max [mm]) | 0.4 - 0.8 | 0.44 - 1.0 | 0.3 - 2.0 | 0.2 - 2.0 |
| Slice SpacingAverage[mm] | 0.54 | 0.63 | 0.79 | 0.76 |
| Pixel Size Range(Min, Max [mm]) | 0.23 - 0.78 | 0.34 - 0.70 | 0.18 - 0.98 | 0.22 - 0.98 |
| Pixel Size Average[mm] | 0.46 | 0.51 | 0.44 | 0.51 |
| Table 5-4 – Software Validation Data | |
|---|---|
| -------------------------------------- | -- |
*NeuroCT/CTa model is used for cardiology cases.
The imaging scanner manufacturers and models used within the validation dataset are listed below.
Table 5-5 – Imaging scanner manufacturers and models used for the validation datasets
| Manufacturer | Model |
|---|---|
| GE Medical Systems | Lightspeed Pro 16Lightspeed Pro 32Revolution CTOptima CT660Discovery CT750 HD |
| Siemens | SOMATOM Definition FlashSOMATOM Definition EdgeSOMATOM Definition ASSOMATOM Definition AS+SOMATOM Perspective |
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Image /page/14/Picture/1 description: The image shows the logo for Axial3D. The word "axial" is in a dark gray color, and the "3D" is in a smaller font and is located to the upper right of the word "axial". A teal diamond is located above the "i" in "axial". Below the logo is the phrase "Patient data made real" in a teal color.
| Manufacturer | Model |
|---|---|
| SOMATOM ForceSensation 16AXIOM-ArtisEmotion 16 | |
| Phillips | IQON Spectral CTiCT 128iCT 256Ingenuity Core 128Brilliance 62 |
| Toshiba | Aquillon PRIMEAquillon PRIME SP |
The AxialM- machine learning model training data used during the algorithm development was explicitly kept separate and independent from the validation data used.
5.9 Conclusions:
Based on the indications for use, product performance, and clinical information provided in this notification, the Axial3D Insight is considered substantially equivalent to the marketed predicate device, Axial3D Cloud Segmentation Service. Both the predicate device and the Axial3D Insight have similar DICOM segmentation and 3D model creation. This 510(k) notification contains the technological characteristics and validation and verification to demonstrate the Axial3D Insight does not raise any different questions regarding safety and effectiveness compared to the predicate, Axial3D Cloud Segmentation Service.
§ 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).