(150 days)
Aibolit 3D+ is intended as a medical imaging system that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from CT and MRI imaging devices. Aibolit 3D+ is intended as software for preoperative surgical planning, patient information and as software for the intraoperative display of the multidimensional digital images. Aibolit 3D+ is designed for use by health care professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.
Aibolit 3D+ is a web-based stand-alone application that can be presented on a computer connected to the internet. Once the enhanced images are created, they can be used by the physician for case review, patient education, professional training and intraoperative reference.
Aibolit 3D+ is a software only device, which processes CT and MR images from a patient to create 3-dimensional images that may be manipulated to view the anatomy from virtually any perspective. The software also allows for transparent viewing of anatomical structures artifacts inside organs such as ducts, vessels, lesions and entrapped calcifications (stones). Anatomical structures are identified by name and differential coloration to highlight them within the region of interest.
The software may help to facilitate the surgeon's decision-making during planning, review and conduct of surgical procedures and, hence, may potentially help them to decrease or prevent possible errors caused by the misidentification of anatomical structures and their positional relationship.
Here's a summary of the acceptance criteria and the study details for the AIBOLIT 3D+ device, extracted from the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are generally focused on the validation of the software's ability to segment anatomical structures accurately and generate 3D models with conservation of shape dimensions and volume. The reported performance indicates that the device met these criteria through validation studies.
| Acceptance Criteria Category | Specific Criteria | Reported Device Performance/Validation |
|---|---|---|
| Software Validation | Software functions as intended and meets user needs. | Software verification and validation performed against defined requirements and user needs. |
| Segmentation Validation | Accurate segmentation of organs/structures. | Segmentation validation of the Customize software performed. R&R study on segmentation of multiple internal organ/structure anatomies performed. AI-based algorithm demonstrated identification of organs/structures based on trained dataset. |
| 3D Model Generation Accuracy | Accurate generation of 3D models from segmented data. | Accuracy study on 3D model generation for multiple organ structures performed. Validation demonstrated conservation of shape dimensions and volume of structures when compared to a "ground truth" accepted standard. |
| MRI Validation | Performance maintained when using MRI images. | Expansion of Software Validation to include MRI validation using multiple organ structures, multiple radiologists, and multiple view perspectives. Conducted per written protocol with pre-determined acceptance criteria. |
| Conservation of Shape/Volume | 3D models accurately represent original dimensions/volume. | The MRI validation demonstrated "conservation of shape dimensions, volume of the structures in a side-by-side testing comparison with a 'ground truth' accepted standard independent of radiologist, organ structure and view perspective." |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Segmentation Training/Evaluation: A dataset of 108 anatomical structures was used for training the AI-based algorithm, obtained from medical images (MRI scans) and their corresponding segmentation. While this is referred to as "trained to identify organs/structures," the subsequent statement about "evaluated from 3 perspectives by 4 radiologists" suggests this dataset may also have served as a test/evaluation set for the AI component. It's not explicitly stated if a separate, distinct test set was used solely for performance evaluation post-training.
- Data Provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective). The type of images are MRI scans. However, the study involved "multiple radiologists and multiple view perspectives" suggesting a multi-center or varied data collection, though specifics are missing.
3. Number of Experts and Qualifications for Ground Truth
- Number of Experts: 4 radiologists were involved in evaluating the AI-based algorithm's segmentations. For the "final patient management decisions" and the manual annotation process, the text specifies "Radiologist (MD)" and "Radiologist."
- Qualifications of Experts: All experts are identified as radiologists. No specific years of experience or subspecialty are mentioned.
4. Adjudication Method for the Test Set
- The AI-based algorithm's segmentations were "evaluated from 3 perspectives by 4 radiologists." This implies a review process.
- After the AI system produces additional segmentations, a radiologist reviews them.
- For image segmentation, the "Radiologist (MD) – Manual annotation is done for all CT and MRI slices with optional use of software as determined by Radiologist and with Radiologist's approval and control." This indicates that the radiologists act as the final decision-makers and can modify annotations.
The precise adjudication method (e.g., majority vote, or whether disagreements were resolved by a super-reviewer) is not explicitly detailed for the evaluation phase. However, the overall process shows a human-in-the-loop approach where radiologists have final say over segmentations.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- Not explicitly stated. The document describes a validation study focused on the device's accuracy against a ground truth and its substantial equivalence to predicate devices. It mentions that "multiple radiologists and multiple view perspectives" were used in the MRI validation, but it does not describe a comparative effectiveness study measuring the improvement of human readers with AI assistance versus without AI assistance. The device is described as assisting the clinician, but no quantitative measure of this assistance's effect on human reader performance is provided.
6. Standalone Performance Study (Algorithm Only)
- Yes, a standalone performance aspect is implied. The AI-based algorithm is described as being "trained to identify organs/structures" and then "produces additional segmentations for review by the radiologist." This indicates that the algorithm itself performs segmentation, which is then subject to human review. The "segmentation validation of the Customize software" and "accuracy study on 3D model generation" would also likely assess the algorithm's performance in isolation before human review. The validation demonstrated "conservation of shape dimensions, volume...independent of radiologist," which points to the intrinsic accuracy of the software's processing.
7. Type of Ground Truth Used
- Expert Consensus / Accepted Standard:
- For the AI training/evaluation, ground truth was "corresponding segmentation" from the MRI scans. This usually implies expert-labeled segmentations.
- For the MRI validation, "conservation of shape dimensions, volume of the structures in a side-by-side testing comparison with a 'ground truth' accepted standard independent of radiologist, organ structure and view perspective" was used. This suggests an established reference or gold standard for anatomical dimensions and volumes.
8. Sample Size for the Training Set
- The AI-based algorithm was trained using a dataset of 108 anatomical structures obtained from MRI scans.
9. How Ground Truth for the Training Set Was Established
- The ground truth for the training set (the "corresponding segmentation") was likely established by experts, as the device's workflow involves radiologists making annotations: "After a radiologist establishes contours, the system produces additional segmentations for review by the radiologist." The expert radiologists are central to the process of creating and validating segmented structures, which would form the basis of the ground truth for training.
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Aibolit Technologies, LLC % Howard Schrayer Regulatory Consultant Howard Schrayer 8 Lookout HILTON HEAD ISLAND SC 29928
Re: K222458
Trade/Device Name: Aibolit 3D+ Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH, LLZ, Dated: December 5, 2022 Received: December 5, 2022
January 12, 2023
Dear Howard Schrayer:
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 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
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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.
Jessica Lamb
Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT 8B: Division of Radiological Imaging Devices and Electronic Products OHT 8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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INDICATIONS FOR USE
DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
Indications for Use
Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2023 See PRA Statement below.
510(k) Number (if known) K222458
Device Name AIBOLIT 3D+
Indications for Use (Describe)
Arbolit 3D+ is intended as a medical imaging system that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from CT and MRI imaging devices. Aibolit 3D+ is intended as software for preoperative surgical planning, patient information and as software for the intraoperative display of the multidimensional digital images. Aibolit 3D+ is designed for use by health care professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.
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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K222458
| Contact: | Howard SchrayerAlbolit Technologies, LLC9616 Moritz WayDelray Beach, FL 33446 |
|---|---|
| Telephone: 609-273-7350hs.ss@lucidmedical.net | |
| Date Prepared: | January 11, 2023 |
| Device Trade Name: | AIBOLIT 3D+ |
| Manufacturer: | Albolit Technologies, LLC9616 Moritz WayDelray Beach, FL 33446 |
| Common Name: | Automated Radiological Image Processing SoftwareMedical image management and processing system |
| Classification: | Class II |
| Product Code: | QIH - LLZ |
| Regulation: | 21 CFR 892.2050 |
| Predicate Devices: | |
| Primary Predicate | Aibolit Technologies, LLC 3D+ System[510(k) K211443]. |
| Reference Predicate | Ceevra, Inc.Ceevra Reveal 2.0Image Processing System[510(k) K173274] |
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Indications for Use:
Aibolit 3D+ is intended as a medical imaging system that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from CT and MR imaging devices. Aibolit 3D+ is intended as software for preoperative surgical planning, training, patient information and as software for the intraoperative display of the multidimensional digital images. Aibolit 3D+ is designed for use by health care professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.
Device Description:
Aibolit 3D+ is a web-based stand-alone application that can be presented on a computer connected to the internet. Once the enhanced images are created, they can be used by the physician for case review, patient education, professional training and intraoperative reference.
Aibolit 3D+ is a software only device, which processes CT and MR images from a patient to create 3-dimensional images that may be manipulated to view the anatomy from virtually any perspective. The software also allows for transparent viewing of anatomical structures artifacts inside organs such as ducts, vessels, lesions and entrapped calcifications (stones). Anatomical structures are identified by name and differential coloration to highlight them within the region of interest.
The software may help to facilitate the surgeon's decision-making during planning, review and conduct of surgical procedures and, hence, may potentially help them to decrease or prevent possible errors caused by the misidentification of anatomical structures and their positional relationship.
Substantial Equivalence and Predicate Devices:
The reason for this submission was to add the processing of images derived from MRI DICOM files to the functioning of the primary Aibolit predicate. This functionality is present in the referenced Ceevra predicate. The device was shown to be substantially equivalent to itself [510(k) K211443] and a previously cleared video image processing system, the Ceevra Reveal 2.0 Image Processing System [510(k) K173274].
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Predicate Comparison Table
| Manufacturer | Albolit Technologies, LLC | Ceevra |
|---|---|---|
| Trade Name | AIBOLIT 3D+Image Processing System | Ceevra Reveal 2.0Image Processing System |
| 510(k) Number | Subject Device - TBD | K173274 |
| Type of Device/Product Code / | Radiological Image ProcessingSystem / QIH - LLZ | Radiological Image ProcessingSystem / LLZ |
| Regulation / Class | 21 CFR 892.2050 – Class II | 21 CFR 892.2050 – Class II |
| Indications forUse | Aibolit 3D+ is intended as amedical imaging system thatallows the processing, review,analysis, communication andmedia interchange of multi-dimensional digital imagesacquired from CT or MRimaging devices. It is alsointended as software forpreoperative surgical planning,training, patient information andas software for theintraoperative display of themultidimensional digital images.Aibolit 3D+ is designed for useby health care professionals andis intended to assist the clinicianwho is responsible for making allfinal patient managementdecisions. | Ceevra Reveal 2.0 is intended asa medical imaging system thatallows the processing, review,analysis, communicationand media interchange of multi-dimensional digital imagesacquired from CT or MR imagingdevices. It is also intended assoftware for preoperative surgicalplanning, and as software for theintraoperative display of theaforementioned multidimensionaldigital images. Ceevra Reveal2.0 is designed for use by healthcare professionals and isintended to assist the clinicianwho is responsible for making allfinal patient managementdecisions. |
| Mechanism ofAction | Capture and enhancement of(DICOM) digital video imagesvia software-based conversionto 2-D and 3-D anatomicalstructure images that can bemanipulated for viewing | Capture and enhancement of(DICOM) digital video images viasoftware-based conversion to 2-D and 3-D anatomical structureimages that can be manipulatedfor viewing |
| Intended Users | Health care professionals | Health care professionals |
| Intended UseEnvironment | Healthcare facilities such ashospitals and clinics | Healthcare facilities such ashospitals and clinics |
| Format ofCaptured Images | DICOM | DICOM |
| Intended Use | ||
| AIBOLIT 3D+ is intended for use as a medical imaging systemthat allows the processing, review, analysis, communicationand media interchange of multi-dimensional digital imagesacquired from CT or MR imaging devices. It is also intended as software forpreoperative surgical planning, and as software for theintraoperative display of multi-dimensional digital images.AIBOLIT 3D+ is designed for use by health care professionalsand is intended to assist the | Intended as a medical imaging system that allows theprocessing, review, analysis, communication and mediainterchange of multi- dimensional digital images acquired from CTor MR imaging devices. It is also intended as software forpreoperative surgical planning, and as software for theintraoperative display of the aforementioned multi-dimensional digital images.Ceevra Reveal 2.0 is designed for use by health careprofessionals and is intended to | |
| clinician who is responsible formaking patient management decisions. | assist the clinician who isresponsible for making all finalpatient management decisions. | |
| Security | Data coded and HIPAAcompliant | Data coded and HIPAAcompliant |
| Form of Device | AIBOLIT 3D+ is a software onlydevice that permits electronicimage uploads, provides imageconversion and allows viewingon a mobile device or standardcomputer monitor. | The Ceevra Reveal 2.0 VideoProcessor is a software onlydevice that permits electronicimage uploads, provides imageconversion and allows viewing ona mobile device or standardcomputer monitor. |
| Image processing | High-definition digital images | High-definition digital images |
| Functions | Generation of 2D and 3Dimages from DICOM dataOrgan segmentation andstructure identificationDimensional and volumereferencesMulti-axis image rotationOrgan transparency | Generation of 2D and 3D imagesfrom DICOM dataOrgan segmentation andstructure identificationDimensional and volumereferencesMulti-axis image rotationOrgan transparency |
| Body contact | None | None |
| User Interface andSystem Work-Flow | Physician uploads DICOMimages and specifies desiredanatomical segments of interestRadiologist annotates sample(segments) images | Physician uploads DICOMimages and specifies desiredanatomical segments of interestImaging technician annotatessample (segments) images |
| Radiologist may use add-onsoftware to facilitate annotationof DICOM images underguidance and full control of theRadiologistRadiologist generates multi-axisrotatable image and returnsoutput file to requestingphysician | Imaging technician generatesmulti-axis rotatable image andreturns output file to requestingphysician | |
| External / InternetConnections | Web-based software | Web-based software |
| CT / MRI ImageUploading | By requesting physician | By requesting physician |
| Other User Inputs | List of organ structures to beannotated and displayed, patientID and demographics | List of organ structures to beannotated and displayed, patientID and demographics |
| ImageSegmentation | By Radiologist (MD) – Manualannotation is done for all CT andMRI slices with optional use ofsoftware as determined byRadiologist and withRadiologist's approval andcontrol | By Imaging Technician – Manualannotation done for all CT andMRI slices - No software usedfor annotation |
| Organidentification | By Radiologist | Unknown proprietary methodused to identify organ structures |
| 3D Imagegeneration | 3D image file generated by 3rdparty software (3D Slicer)following Radiologist review andapproval of annotation | 3D image file generated by 3rdparty software |
| Organ structureidentification | Proprietary software assignscolor coding to each structureidentified by Radiologist anddisplays color-coded image withlabeled key to color/structureidentity | Proprietary software assignscolor coding to each structureidentified by imaging technicianand displays color-coded imagewith key to color/structure identity |
| Image editingpermission | Only the radiologist can editimages following review – Userphysicians cannot edit images –Physicians have option to showor hide organs on display | Imaging technician can editimages generated by the systemsoftware – User physicianscannot edit images - Physicianshave option to show or hideorgans on display |
| Device OutputDevices | 3D image can be displayed onstandard monitor or anotherappropriate display | 3D image can be displayed onstandard monitor, smart phone(with separate software) orVirtual Imaging 3D headset |
| Supplementaloutputs | Organ structure dimensions,volume, organ labels, patient ID,image date and demographics | Organ structure dimensions,volume, organ labels, patient IDand demographics |
| Output imagemanipulation byuser | Physician user can show or hideindividual organ structures,zoom capability, rotationalcapability and transparencycapability | Physician user can show or hideindividual organ structures, zoomcapability, rotational capability,transparency capability (currentversion) |
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Performance Testing:
Non-clinical performance data was included in the 510(k) submission to demonstrate that the Aibolit software has been validated for its intended use and to support substantial equivalence to the predicate device.
Software verification and validation were performed, and documentation was included in this submission in accordance with FDA Guidance "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices". This includes verification against defined requirements, and validation against user needs. In addition, performance testing included 1) segmentation validation of the Customize software, 2) Repeatability and Reproducibility (R&R) study on the segmentation of multiple internal organ/structure anatomies and 3) accuracy study on 3D model generation for multiple organ structures.
For image segmentation, the device includes optional artificial intelligence including machine learning followed by review by a radiologist. The Al-based algorithm is based on a system that has been trained to identify organs/structures using a dataset of 108 anatomical structures as obtained from medical images (MRI scans) and their corresponding segmentation. The images were evaluated from 3 perspectives by 4 radiologists. After a radiologist establishes contours. the system produces additional seqmentations for review by the radiologist.
The following documentation was previously submitted.
- Hardware Requirements Level of Concern Statement Software Description Architecture Design User Manual and Instructions for Use Software Design Specification Risk Analysis Traceability Analysis Software Validation Report Usability Evaluation Software Development Lifecycle Unresolved Anomalies Cybersecurity
Additional Testing
Expansion of Software Validation to include MRI validation using multiple organ structures, multiple radiologists and multiple view perspectives. The validation was conducted in accordance with a written protocol with pre-determined acceptance criteria. The validation demonstrated conservation of shape dimensions, volume of the structures in a side-by-side testing comparison with a "ground truth" accepted standard independent of radiologist, organ structure and view perspective.
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Conclusion
AIBOLIT 3D+ is substantially equivalent to the previously cleared Aibolit 3D+ system (K211443) and to the Ceevra Reveal 2.0 Image Processing System (K173274) with respect to intended use, principle of operation, general technological characteristics and performance.
§ 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).