Acorn Segmentation is intended for use as a software interface and image segmentation system for the transfer of CT or CTA medical images to an output file. Acorn Segmentation is also intended for measuring and treatment planning. The Acorn Segmentation output can also be used for the fabrication of the output file using additive manufacturing methods, Acorn 3DP Models. The physical replica can be used for diagnostic purposes in the field of musculoskeletal and craniomaxillofacial applications.

Acorn Segmentation and 3DP Models should be used in conjunction with expert clinical judgment.

Device Description

Acorn Segmentation is an image processing software that allows the user to import, visualize and segment medical images, check and correct the segmentations, and create digital 3D models. The models can be used in Acorn Segmentation for measuring, treatment planning and producing an output file to be used for additive manufacturing (3D printing). Acorn Segmentation is structured as a modular package. This includes the following functionality:

Importing medical images in DICOM format
Viewing images and DICOM data
Selecting a region of interest using generic segmentation tools
Segmenting specific anatomy using dedicated semi-automatic tools or automatic algorithms
Verifying and editing a region of interest
Calculating a digital 3D model and editing the model
Measuring on images and 3D models
Exporting 3D models to third-party packages

Acorn Segmentation contains both machine learning based auto-segmentation as well as semi-automatic and manual segmentation tools. The auto-segmentation tool is only intended to be used for thoracic and lumbar regions of the spine (T1-T12 and L1-L5). Semi-automatic and manual segmentation tools are intended to be used for all musculoskeletal and craniomaxillofacial anatomy.

Acorn 3DP Model is an additively manufactured physical replica of the digital 3D model generated in Acorn Segmentation. The output file from Acorn Segmentation is used to additively manufacture the Acorn 3DP Model.

AI/ML Overview

The provided text describes the acceptance criteria and the study that proves the device meets those criteria for the Acorn 3D Software (AC-SEG-4009) and Acorn 3DP Model (AC-101-XX).

Here's the breakdown:

1. A table of acceptance criteria and the reported device performance

Acceptance Criteria (Metric)	Target Acceptance Criteria	Reported Device Performance
Geometric accuracy of digital models (Dice-Sorensen Coefficient) - Automatic Segmentation (Thoracic and Lumbar spine)	Average Dice-Sorensen Coefficient ≥ 0.93	Average Dice-Sorensen Coefficient > 0.93
Geometric accuracy of digital models (Dice-Sorensen Coefficient) - Semi-automatic and Manual Segmentation (Musculoskeletal & Craniomaxillofacial bone)	Average Dice-Sorensen Coefficient ≥ 0.93	Average Dice-Sorensen Coefficient > 0.93
Geometric accuracy of physical replicas (Mean Deviation)	Mean Deviation < 1mm	Mean Deviation < 1mm

2. Sample size(s) used for the test set and the data provenance

The document does not specify the exact sample size for the test set nor the data provenance (e.g., country of origin, retrospective or prospective). It only states that software verification and validation included bench testing for geometric accuracy.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

The document does not explicitly state the number of experts or their qualifications used to establish the ground truth for the test set.

4. Adjudication method for the test set

The document does not mention any specific adjudication method (e.g., 2+1, 3+1) for the test set.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

A Multi-Reader, Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or described. The study primarily focused on the algorithmic performance against predicate devices and physical models, rather than human reader improvement with AI assistance.

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

Yes, a standalone (algorithm only) performance study was conducted. The "Accuracy of automatic segmentation" was evaluated, which by definition means the algorithm's performance without direct human control. The bench testing for geometric accuracy directly assesses the algorithm's output against a reference.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The ground truth for the geometric accuracy of digital models was established by comparing the Acorn Segmentation output against "predicate device segmentations." This implies that the segmentations generated by previously cleared and accepted devices served as the reference standard. For physical replicas, the ground truth was the "digital models" themselves.

8. The sample size for the training set

The document states that the "Acorn Segmentation contains both machine learning based auto-segmentation... Using a collection of images and masks as a training dataset for machine-learning segmentation algorithm". However, the specific sample size for the training set is not provided in this document.

9. How the ground truth for the training set was established

The document states that the ground truth for the training set was established by "using a collection of images and masks as a training dataset for machine-learning segmentation algorithm." This implies that pre-existing segmented images (masks) were used as the ground truth for training the machine learning model. The method by which these initial "masks" were generated (e.g., manual segmentation by experts, semi-automatic segmentation, etc.) is not detailed in this document.

Summary

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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which consists of the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG" in blue, with the word "ADMINISTRATION" underneath. The logo is simple and professional, and it is easily recognizable.

Mighty Oak Medical Mark Wylie VP Quality and Regulatory 750 W. Hampden Ave Suite 120 Englewood, Colorado 80110

July 12, 2024

Re: K234009

Trade/Device Name: Acorn 3D Software (AC-SEG-4009); Acorn 3DP Model (AC-101-XX) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH, LLZ Dated: June 12, 2024 Received: June 12, 2024

Dear Mark Wylie:

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.

FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an

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established PCCP granted pursuant to section 515C(b)(2) of the Act. Under 21 CFR 807.81(a)(3), a new premarket notification is required if there is a major change or modification in the intended use of a device, or if there is a change or modification in a device that could significantly affect the safety or effectiveness of the device, e.g., a significant change or modification in design, material, chemical composition, energy source, or manufacturing process. Accordingly, if deviations from the established PCCP result in a major change or modification in the intended use of the device, or result in a change or modification in the device that could significantly affect the safety or effectiveness of the a new premarket notification would be required consistent with section 515C(b)(1) of the Act and 21 CFR 807.81(a)(3). Failure to submit such a premarket submission would constitute adulteration and misbranding under sections 501(f)(1)(B) and 502(o) of the Act, respectively.

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 (OS) 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 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-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 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.

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-device-safety/medical-device-reportingmdr-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/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-regulatory

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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,

Jessica Lamb

Jessica Lamb Assistant Director DHT8B: Division of Radiologic 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) K234009

Device Name Acorn 3D Software (AC-SEG-4009) Acorn 3DP Model (AC-101-XX)

Indications for Use (Describe)

Acorn Segmentation and 3DP Models should be used in conjunction with 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/4/Picture/2 description: The image shows the logo for Mighty Oak. The logo features three green curved lines above the words "MIGHTY OAK" in blue. The curved lines are arranged in a way that suggests movement or growth.

MEDICA

Acorn 3D Software (AC-SEG-4009) and Acorn 3DP Model (AC-101-XX)

Submitter:

Mighty Oak Medical 750 W. Hampden Ave., Suite 120 Englewood, CO 80110 (720) 398-9703

Contact:	Mark A. Wylie, VP of Quality and Regulatory
Date Prepared:	12JUL2024

Device

Trade Name:	Acorn 3D Software (AC-SEG-4009); Acorn 3DP Model (AC-101-XX)
Common Name:	Image processing system
Device Classification:	Class II
Regulation, Name:	21 CFR 892.2050, Medical image management and processing system
Device Product Code:	QIH, LLZ

Type of 510(k)

Original Submission: Traditional

Predicate Device(s):

Acorn 3D Software (AC-SEG-4009); Acorn 3DP Model (AC-101-XX)

510(k)	Product Code	Trade Name	Manufacturer
Primary Predicate Device
K183105	LLZ	Mimics Medical	Materialise NV
Subsequent Predicate Device
K183489	LLZ	D2P	3D Systems, Inc

Description

. Importing medical images in DICOM format
· Viewing images and DICOM data
· Selecting a region of interest using generic segmentation tools
· Segmenting specific anatomy using dedicated semi-automatic tools or automatic algorithms
· Verifying and editing a region of interest
· Calculating a digital 3D model and editing the model
· Measuring on images and 3D models
· Exporting 3D models to third-party packages

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Acorn Seamentation contains both machine learning based auto-seamentation as well as semiautomatic and manual segmentation tools. The auto-segmentation tool is only intended to be used for thoracic and lumbar regions of the spine (T1-T12 and L1-L5). Semi-automatic and manual seamentation tools are intended to be used for all musculoskeletal and craniomaxillofacial anatomy. The following table provides a definition and the anatomical location(s) for each tool's intended use.

	Automatic	Semi-Automatic	Manual
Definition	Algorithmic with little or nodirect human control	A combination ofalgorithmic and directhuman control	Directly controlled by ahuman
Tool Type	Machine Learningalgorithm used toautomatically segmentindividual vertebrae	Algorithmic based toolsthat do not incorporatemachine learning.	Manual tools requiring userinput.
AnatomicalLocation (s)	Spinal anatomy:· Thoracic (T1-T12)• Lumbar (L1-L5)	Musculoskeletal &craniomaxillofacial bone:· Short· Long· Flat· Sesamoid· Irregular	Musculoskeletal &craniomaxillofacial bone:· Short· Long· Flat· Sesamoid· Irregular

Indications for Use

Acorn Seamentation is intended for use as a software interface and image segmentation system for the transfer of CT or CTA medical images to an output file. Acorn Segmentation is also intended for measuring and treatment planning. The Acorn Seamentation output can also be used for the fabrication of physical replicas of the output file using additive manufacturing methods, Acorn 3DP Models. The physical replica can be used for diagnostic purposes in the field of musculoskeletal and craniomaxillofacial applications.

Acorn Segmentation and 3DP Models should be used in conjunction with expert clinical judgment.

Materials

Acorn 3DP materials used have been tested and shown to be biocompatible in accordance with ISO 10993-1. The material used to manufacture Acorn 3DP Models is a PA-12 polymer powder for use in HP multi-jet fusion systems.

Performance Data

Software verification and validation were performed and documentation was provided following the "Guidance for the Content of Premarket Submissions for Software in Medical Devices". This includes verification against defined requirements, and validation against user needs. Both end-user validation and bench testing were performed.

The geometric accuracy of digital models created in the subject device, Acorn Segmentation, was assessed via bench testing of automatic, semi-automatic, and manual segmentation methods. Accuracy of automatic segmentation was evaluated for its intended use on Thoracic (T1-T12) and Lumbar (L1-L5) anatomy. Accuracy of semi-automatic and manual segmentation methods was evaluated for their intended for use on musculoskeletal and craniomaxillofacial anatomy. All segmentations were evaluated against predicate device segmentations using Dice-Sorenson coefficient. Testing of automatic, semi-automatic, and manual segmentation methods each exceeded an average Dice-Sorenson coefficient of 0.93. All deviations were within the acceptance criteria. This shows that for creating digital models, Acorn Segmentation is substantially equivalent to the predicate device.

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The geometric accuracy of physical replicas (produced by 3D printing digital models) was also assessed. This was conducted for various representative musculoskeletal and craniomaxillofacial models. Testing showed that the physical models can be printed accurately at less than 1mm mean deviation when compared to the digital models.

In conclusion, all performance testing conducted demonstrated device performance and substantial equivalence to the predicate device.

Technological Characteristics

The following technological characteristics of the subject Acorn Seamentation & 3DP Model are equivalent to the predicate devices. These include:

. Importing medical images in DICOM format
. Viewing images and DICOM data
Selecting a region of interest using generic segmentation tools ●
Segmenting specific anatomy using dedicated semi-automatic tools or fully automatic ● algorithms
. Verifying and editing a region of interest
. Calculating a digital 3D model and editing the model
Measuring on images and 3D models ●
. Exporting 3D models to third-party packages

Technological characteristics which are different have been supported with descriptive information and/or performance data. Therefore the fundamental scientific technology of Acorn Segmentation & 3DP Model is the same as the previously cleared device.

Substantial Equivalence Comparison Table

The table below provides a descriptive comparison of the similarities and differences for the subject and predicate devices. The items marked bold in the table highlight the differences between the Acorn Segmentation & 3DP Model and the predicate (Materialise's Mimics Medical, K183105).

Device→	Acorn Segmentation(K234009)	Mimics Medical(K183105)	D2P(K183489)
Features↓
Premarketnotification	K234009	K183105	K183489
Manufacturer	Mighty Oak Medical	Materialise N.V.	3D Systems
Indications for UseStatement	Acorn Segmentation is intended for use as asoftware interface and image segmentationsystem for the transfer of CT or CTA medicalimaging information to an output file. AcornSegmentation is also intended for measuring andtreatment planning. The Acorn Segmentationoutput can also be used for the fabrication ofphysical replicas of the output file using additivemanufacturing methods, Acorn 3DP Models. Thephysical replica can be used for diagnosticpurposes in the field of musculoskeletal andcraniomaxillofacial applications.Acorn Segmentation and 3DP Models should beused in conjunction with expert clinical judgment.	Mimics is intended foruse as a softwareinterface and imagesegmentation systemfor the transfer ofmedical imaginginformation to anoutput file. MimicsMedical is alsointended formeasuring andtreatment planning.The Mimics Medicaloutput can be usedfor the fabrication ofphysical replicas ofthe output file usingtraditional or additivemanufacturingmethods.The physical replicacan be used fordiagnostic purposes in	The D2P software isintended for use as asoftware interfaceand imagesegmentation systemfor the transfer ofDICOM imaginginformation from amedical scanner toan output file. It is alsointended as pre-operative software forsurgical planning. Forthis purpose, theoutput file may beused to produce aphysical replica. Thephysical replica isintended foradjunctive use alongwith other diagnostictools and expertclinical judgement for
Device→Features↓	Acorn Segmentation(K234009)	Mimics Medical(K183105)	D2P(K183489)
		the field oforthopaedic,maxillofacial andcardiovascularapplications.Mimics Medicalshould be used inconjunction withexpert clinicaljudgment.	diagnosis, patientmanagement, and/ortreatment selection ofcardiovascular,craniofacial,gastrointestinal,genitourinary,neurological, and/ormusculoskeletalapplications.
General intendeduse	Acorn Segmentation is image processing softwarethat allows the user to import, visualize andsegment medical images, check and correct thesegmentations, and create digital 3D models.	Mimics Medical isimage processingsoftware that allowsthe user to import,visualize and segmentmedical images,check and correct thesegmentations, andcreate digital 3Dmodels.	D2P is imageprocessing softwarethat allows the user toimport, visualize andsegment medicalimages, check andcorrect thesegmentations, andcreate digital 3Dmodels.
ProductClassification	System, Image processing, Radiological	System, Imageprocessing,Radiological	System, Imageprocessing,Radiological
Regulatory Class	Class II	Class II	Class II
RegulationNumber	892.2050	892.2050	892.2050
Product Code	QIH, LLZ	LLZ	LLZ
DeviceDescription	Acorn Segmentation is an image processingsoftware that allows the user to import, visualizeand segment medical images, check andcorrect the segmentations, and create digital 3Dmodels. The models can be used in AcornSegmentation for measuring, treatment planningand producing an output file to be used foradditive manufacturing (3D printing). AcornSegmentation is structured as a modularpackage. This includes the following functionality:Importing medical images in DICOM format Viewing images and DICOM data Selecting a region of interest using genericsegmentation tools Segmenting specific anatomy usingdedicated semi-automatic tools or fullyautomatic algorithms Verifying and editing a region of interest Calculating a digital 3D model and editingthe model Measuring on images and 3D models Exporting 3D models to third-party packages Acorn Segmentation contains both machinelearning based auto-segmentation as well assemi-automatic and manual segmentation tools.The auto-segmentation tool is only intended to beused for thoracic and lumbar regions of the spine(T1-T12 and L1-L5). Semi-automatic and manualsegmentation tools are intended to be used for allmusculoskeletal and craniomaxillofacialanatomy. The following table provides a definition	Mimics Medical isimage processingsoftware that allowsthe user to import,visualize and segmentmedical images,check and correct thesegmentations, andcreate digital 3Dmodels. The modelscan be used in MimicsMedical formeasuring, treatmentplanning andproducing an outputfile to be used foradditivemanufacturing (3Dprinting). MimicsMedical also hasfunctionality for linkingto third party softwarepackages. MimicsMedical is structuredas a modularpackage. This includesthe followingfunctionality:Importingmedical imagesin DICOM formatand otherformats (such as	The D2P software is astand-alone modularsoftware packagethat providesadvancedvisualization of DICOMimaging data. Thismodular packageincludes, but is notlimited to the followingfunctions:DICOM viewerand analysis Automatedsegmentation Editing and pre-printing Seamlessintegration with3D Systemsprinters Seamlessintegration with3D Systemssoftwarepackages Seamlessintegration withVirtual Realityvisualization fornon-diagnosticuse.

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Device→Features↓	Acorn Segmentation(K234009)and the anatomical location(s) for each tool'sintended use.			Mimics Medical(K183105)	D2P(K183489)
	Automatic	Semi-Automatic	Manual	BMP, TIFF, JPGand raw images) Viewing imagesand DICOM data Selecting aregion of interestusing genericsegmentationtools Segmentingspecific anatomyusing dedicatedsemi-automatictools or fullyautomaticalgorithms Verifying andediting a regionof interest Calculating adigital 3D modeland editing themodel Measuring onimages and 3Dmodels Exportingimages,measurementsand 3D modelsto third-partypackages Planningtreatments(surgical cutsetc.) on the 3Dmodels Interfacing withpackages forFinite ElementAnalysis Creating Pythonscripts toautomateworkflows
	Definition	Algorithmicwith little orno directhumancontrol	Acombinationofalgorithmicand directhumancontrol	Directlycontrolledby ahuman
	ToolType	MachineLearningalgorithmused toautomat-icallysegmentindividualvertebrae	Algorithmicbased toolsthat do notincorporatemachinelearning.	Manualtoolsrequiringuser input.
	Anatom-icalLocation(s)	Spinalanatomy:• Thoracic(T1-T12)• Lumbar(L1-L5)	Musculo-skeletal &cranioma-xillofacialbone:• Short• Long• Flat• Sesamoid• Irregular	Musculo-skeletal &craniuma-xillofacialbone:• Short• Long• Flat• Sesa-moid• Irreg-ular
Technologicalcharacteristics	Acorn Segmentation is a standalone modularsoftware package. This module includes, but isnot limited to the following functions:Image Import• Importing medical images in DICOM formatImage Processing• Processing of images with common noise-reduction filters• Editing of spatial arrangement of imagesVisualization• Viewing images and DICOM dataSegmentation• Selecting a region of interest using genericsegmentation tools• Segmenting specific anatomy using			Mimics Medical isstructured as amodular package.This includes thefollowing functionality:Image Import• Importingmedical imagesin DICOM formatand other formats(such as BMP,TIFF, JPG and rawimages)Image Processing• Processing ofimages withcommon noise-	D2P is structured as amodular package.This includes thefollowing functionality:Image Import• Importingmedical imagesin DICOM formatand otherformats (such asBMP, TIFF, JPGand raw images)Image Processing• Processing ofimages withcommon noise-reduction filters

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Device→Features↓	Acorn Segmentation(K234009)	Mimics Medical(K183105)	D2P(K183489)
	Segmenting specific vertebral anatomy using machine-learning-based fully automatic algorithms Verifying and editing a region of interest	Editing of spatial arrangement of images Processing of imaging data for removal of common artifacts (e.g. scatter)	Editing of spatial arrangement of images
Measurement	Measuring on images and 3D models	Visualization Viewing images and DICOM data	Visualization Viewing images and DICOM data
Image Export	Exporting images and 3D models to third-party packages	Segmentation Selecting a region of interest using generic segmentation tools Segmenting specific anatomy using dedicated semi-automatic tools or fully automatic algorithms Verifying and editing a region of interest	Segmentation Selecting a region of interest using generic segmentation tools Segmenting specific anatomy using dedicated semi-automatic tools or fully automatic deep learning tools Verifying and editing a region of interest
3D Models	Calculating a digital 3D model and editing the model Smoothing a 3D model Importing 3D models	Measurement Measuring on images and 3D models	Measurement Measuring on images and 3D models
		Image Export Exporting images, measurements and 3D models to third-party packages	Image Export Exporting images and 3D models to third-party packages
		3D Models Calculating a digital 3D model and editing the model Wrapping a 3D model Smoothing a 3D model Importing 3D models	3D Models Calculating a digital 3D model and editing the model Smoothing a model Importing 3D models
Treatment Planning	Importing of third-party STLs to visualize planned interactions with anatomy as represented in DICOM images	Treatment Planning Importing of third-party STLs to visualize planned interactions with anatomy as represented in DICOM images	Treatment Planning Importing of third-party STLs to visualize planned interactions with anatomy as represented in DICOM images Planning treatments (surgical cuts etc.) on the 3D models
Other features	Using a collection of images and masks as a training dataset for machine-learning segmentation algorithm		Other features
Device→Features↓	Acorn Segmentation(K234009)	Mimics Medical(K183105)	D2P(K183489)
		Other features Interfacing withpackages forFinite ElementAnalysis Creating Pythonscripts toautomateworkflows
Physical Model	The Acorn Segmentation output can be used forthe fabrication of physical replicas of the outputfile using additive manufacturing methods. Thephysical replica can be used for diagnosticpurposes in the field of musculoskeletal andcraniomaxillofacial applications.	The Mimics Medicaloutput can be usedfor the fabrication ofphysical replicas ofthe output file usingtraditional or additivemanufacturingmethods. The physicalreplica can be usedfor diagnosticpurposes in the field oforthopedic,maxillofacial andcardiovascularapplications.	The D2P output filemay be used toproduce a physicalreplica. The physicalreplica is intended foradjunctive use alongwith other diagnostictools and expertclinical judgement fordiagnosis, patientmanagement, and/ortreatment selection ofcardiovascular,craniofacial,gastrointestinal,genitourinary,neurological, and/ormusculoskeletalapplications.

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Conclusion

Minor differences in intended use and technological characteristics exist, but performance data demonstrates that Acorn Segmentation & 3DP Model is as safe and effective, and performs as well as the predicate device for its intended use.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

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