K171350 · Radformation, Inc. · IYE · Nov 29, 2017 · Radiology
Device Facts
Record ID
K171350
Device Name
Collision Check
Applicant
Radformation, Inc.
Product Code
IYE · Radiology
Decision Date
Nov 29, 2017
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.5050
Device Class
Class 2
Attributes
Software as a Medical Device
Indications for Use
CollisionCheck is intended to assist radiation treatment planners in predicting when a treatment plan might result in a collision between the treatment machine and the patient or support structures.
Device Story
CollisionCheck (model RADCO) is a software plugin for Varian Eclipse Treatment Planning System; assists radiation treatment planners in identifying potential collisions between treatment machines and patients/support structures. Input: CT, structure set, and treatment plan data accessed via Eclipse Scripting API. Operation: models linac gantry as cylinder; supports additional applicators (stereotactic radiosurgery cones as cylinders, electron applicators as rectangular prisms); tests thousands of sample points against CT data (Hounsfield-Unit based) and defined structures. Output: collision count, 3D visualization of simulated treatment unit/patient/structures, and 2D axial slice views. Usage: clinical environment by trained radiation oncology personnel. Benefit: enables manual inspection of collision risks, improving safety over structure-set-only analysis; reduces potential for manual errors associated with DICOM export/import workflows.
Clinical Evidence
No clinical data. Verification testing performed using pass/fail criteria to ensure software functionality.
Technological Characteristics
Software-only device; runs as a DLL plugin on Windows OS within Varian Eclipse TPS. Sensing/modeling principle: geometric modeling of linac gantry (cylinder), SRS cones (cylinder), and electron applicators (rectangular prism). Connectivity: integrates via Eclipse Scripting API. Software: rule-based collision detection algorithm.
Indications for Use
Indicated for radiation treatment planners to predict potential collisions between treatment machines and patients or support structures during radiation therapy planning.
Regulatory Classification
Identification
A medical charged-particle radiation therapy system is a device that produces by acceleration high energy charged particles (e.g., electrons and protons) intended for use in radiation therapy. This generic type of device may include signal analysis and display equipment, patient and equipment supports, treatment planning computer programs, component parts, and accessories.
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November 29, 2017
RADformation, Inc. % Mr. Kurt Sysock Co-founder/CEO 335 Madison Avenue, 16th Floor NEW YORK NY 10017
Re: K171350
Trade/Device Name: CollisionCheck Regulation Number: 21 CFR 892.5050 Regulation Name: Medical charged-particle radiation therapy system Regulatory Class: II Product Code: IYE Dated: October 4, 2017 Received: October 11, 2017
Dear Mr. Sysock:
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. 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); good manufacturing practice requirements as set forth in the quality systems (OS) regulation (21 CFR Part 820); and if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
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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 http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm for the CDRH's Office of Surveillance and Biometrics/Division of Postmarket Surveillance.
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/DeviceRegulationandGuidance/) and CDRH Learn (http://www.fda.gov/Training/CDRHLearn). 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 (http://www.fda.gov/DICE) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Robert Ochs, Ph.D. Director Division of Radiological Health Office of In Vitro Diagnostics and Radiological Health Center for Devices and Radiological Health
Enclosure
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# Indications for Use
510(k) Number (if known)
K171350/S001
Device Name CollisionCheck
Indications for Use (Describe)
CollisionCheck is intended to assist radiation treatment planners in predicting when a treatment plan might result in a collision between the treatment machine and the patient or support structures.
Type of Use (Select one or both, as applicable)
X Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
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This 510(k) Summary has been created per the requirements of the Safe Medical Device Act (SMDA) of 1990, and the content is provided in conformance with 21 CFR Part 807.92.
## 3.1. Submitter's Information
| Table 1 : Submitter's Information | |
|-----------------------------------|------------------------------------------------------|
| Submitter's Name: | Kurt Sysock |
| Company: | Radformation, Inc. |
| Address: | 335 Madison Avenue, 16th Floor<br>New York, NY 10017 |
| Contact Person: | Alan Nelson<br>Chief Science Officer, Radformation |
| Phone: | 518-888-5727 |
| Fax: | -------- |
| Email: | anelson@radformation.com |
| Date of Summary Preparation | 05/01/2017 |
### 3.2. Device Information
| | Table 2 : Device Information |
|-----------------------|-----------------------------------------------------------------------------------------|
| Trade Name: | CollisionCheck |
| Common Name: | Oncology Information System |
| Classification Name: | Class II |
| Classification: | Medical charged-particle radiation therapy system,<br>dosimetric quality control system |
| Regulation Number: | 892.5050 |
| Product Code: | IYE |
| Classification Panel: | Radiology |
## 3.3. Predicate Device Information
Mobius3D (K153014)
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#### 3.4. Device Description
The CollisionCheck device (model RADCO) is software intended to assist users to identify where collisions between the treatment machine and the patient or support structures may occur in a treatment plan. The treatment plans are obtained from the Eclipse Treatment Planning System (also referred to as Eclipse TPS) of Varian Medical Systems. CollisionCheck runs as a dynamic link library (DLL) plugin to Varian Eclipse.
It is designed to run on the Windows Operating System. CollisionCheck performs calculations on the plan obtained from Eclipse TPS (Version 12 (K131891), Version 13.5 (K141283), and Version 13.7 (K152393) which is a software used by trained medical professionals to install and simulate radiation therapy treatments for malignant or benign diseases.
#### 3.5. Indications for Use
CollisionCheck is intended to assist radiation treatment planners in predicting when a treatment plan might result in a collision between the treatment machine and the patient or support structures.
### 3.6. Technological Characteristics
CollisionCheck (Subject Device) makes use of a Predicate Device, Mobius3D (K153014) for substantial equivalence comparison.
#### 3.6.1. CollisionCheck vs. Mobius3D (K153014)
Mobius3D provides the following feature (as described on the manufacturer's website at http://mobiusmed.com/mobius3d/ as of March 27, 2017): "Deliverability Analysis: Confirm Before Your Patient Arrives. Mobius3D performs a virtual delivery of the plan and verifies that no gantry collision or violation of your machine's delivery parameters is predicted."
CollisionCheck likewise simulates the plan and verifies that no gantry (treatment machine) collisions occur. The main difference between the implementation of that feature by Mobius3D and CollisionCheck is that Mobius3D takes DICOM plan data as input in order to perform the virtual delivery while CollisionCheck obtains treatment plan information from the Varian Medical Systems Eclipse Treatment Planning System through its scripting API. Furthermore, CollisionCheck does not have any other features besides the gantry collision check while Mobius3D includes many other features that are not related to the gantry collision check.
From Mobius3D's Intended Use statement: "Mobius3D software is used for quality assurance, treatment plan verification, and patient alignment and anatomy analysis in radiation therapy." CollisionCheck is likewise a quality assurance and treatment plan verification tool, but one with the very specific scope of checking for potential treatment machine collisions that might occur in a treatment plan.
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| Table 3: Substantial Equivalence CollisionCheck vs. Mobius3D | | | |
|--------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------|
| Parameters | Subject Device: CollisionCheck<br>Radformation | Predicate Device: Mobius3D<br>(K153014) | Equivalence |
| Indications for<br>use | Used to assist radiation<br>treatment planners in predicting<br>when a treatment plan might<br>result in a collision between the<br>treatment machine and the<br>patient or support structures | Used for quality assurance,<br>treatment plan verification, and<br>patient alignment and anatomy<br>analysis in radiation therapy. | Subject<br>Device is a<br>subset of the<br>Predicate<br>Device |
| Pure software | Yes | Yes | Equivalent |
| Intended users | Trained radiation oncology<br>personnel | Trained radiation oncology<br>personnel | Equivalent |
| OTC/Rx | Rx | Rx | Equivalent |
| CollisionCheck vs. Mobius3D Deliverability Analysis | | | |
| Input | CT, Structure Set, and<br>Treatment Plan data accessed<br>through the Eclipse Scripting<br>API | DICOM files containing CT,<br>Structure Set, and Treatment<br>Plan (including treatment field<br>parameters) data | Minor<br>Differences |
| Functionality | Simulates the plan and predicts<br>whether that no gantry collisions<br>occur with patient or support<br>structures. | Performs a virtual delivery of the<br>plan and verifies that no gantry<br>collision is predicted. | Equivalent |
| Simulation<br>Details | CollisionCheck calculates<br>gantry clearance by modeling<br>the linac as a cylinder with a<br>user-configured value for<br>distance between isocenter and<br>the face of the gantry.<br>CollisionCheck also supports<br>additional applicators:<br>Stereotactic radiosurgery cones<br>(also modeled as a cylinder)<br>and Electron Applicators<br>(modeled as a rectangular<br>prism). | Mobius3D calculates gantry<br>clearance in Plan Checks by<br>modeling the linac as a cylinder<br>with a user-configured value for<br>distance between isocenter and<br>the face of the gantry. | Minor<br>Differences |
| Output | CollisionCheck tests thousands<br>of sample points against CT<br>data and patient and couch<br>structures and reports the<br>number of sample points that<br>resulted in a collision.<br>CollisionCheck also displays<br>these sample point test results | Mobius3D determines the closest<br>distance between the treatment<br>head and the patient/couch for<br>each beam and displays a<br>warning if this distance is <= 3cm<br>and an alert if the clearance is<br><= 0cm. | Minor<br>Differences |
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| with a 3D display and an axial<br>' 2D image plane viewer for the<br>user to inspect the results. |
|---------------------------------------------------------------------------------------------------|
|---------------------------------------------------------------------------------------------------|
### 3.7. Differences Discussion
#### Indications for use
Mobius3D is a quality assurance platform that includes a wide variety of plan checks, including a collision check. CollisionCheck is highly specialized to only perform the collision check function, and therefore CollisionCheck performs only a subset of the uses provided by the predicate device Mobius3D. The fact that CollisionCheck performs only this specialized function does not raise any questions regarding safety and effectiveness in that this specialized function can stand alone and independent of all other functions contained in the Mobius3D predicate device.
#### Input
Mobius3D requires the CT, Structure Set, and Treatment Plan data to be exported from a treatment planning system in DICOM format. CollisionCheck obtains the same data directly from the Eclipse treatment planning system through Eclipse's Scripting API. This does not raise any new questions regarding safety and effectiveness in that, relative to DICOM export/import, the Eclipse Scripting API layer is closer to the original data and requires less manual user input, therefore exposing fewer issues associated with errors in the DICOM import/export process as well as user error in accidentally exporting or importing the wrong DICOM files.
#### Simulation Details
Mobius3D and CollisionCheck both simulate the gantry face and head as a cylinder, but CollisionCheck also supports the addition of applicators (which are mounted to the face of the gantry). These are simulated using simple geometries that are no more difficult to simulate than the cylinder used in the gantry face and therefore this difference does not raise new questions regarding safety and effectiveness. The simulation of stereotactic radiosurgery cones and electron applicators increases safety and effectiveness of the device by assisting the treatment planner to predict and avoid collisions with those applicators.
#### Output
Mobius3D determines the closest distance between the patient and support structures and the simulated gantry head and gives an alert or warning based on the result.
CollisionCheck tests not only patient and support structures for collisions (referred to in CollisionCheck as Structure-based Collisions) but also tests the simulated geometry against the Hounsfield-Unit CT data (referred to in CollisionCheck as HU-based Collisions) to report to the user the number and location of the sample points that resulted in either of those collisions. HU-based collision test adds an extra layer of safety in that the treatment setup, patient geometry, and support structures may not be fully
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described by the structure set and by testing the HU values may detect a collision that would otherwise be missed if only the structure set is considered.
In addition to displaying the number of sample point collisions detected, CollisionCheck also displays a 3D model of the simulated treatment unit with the patient and support structure geometry that allows the user to visualize where the treatment unit will be in the treatment. This increases safety significantly in that Mobius3D and CollisionCheck both are limited in the accuracy of their collision check by the data available in the CT and structure set, and where patient anatomy falls outside of the bounds of that data, only manual inspection can determine whether there is a collision risk in those instances. The 3D display provided by CollisionCheck makes it significantly easier and more accurate to manually inspect the treatment geometry for these potential issues.
CollisionCheck also provides an 2D axial slice view of the collision check results which allows the user to see where the treatment machine will be for each treatment field within the actual CT data to assist the user in identifying exactly where the collisions would occur within the patient or support structures.
These differences in output do not raise any questions regarding the safety and effectiveness of CollisionCheck relative to the predicate device Mobius3D.
#### 3.8. Performance Data
As with the Predicate Device, no clinical trials were performed for CollisionCheck. Verification tests were performed to ensure that the software works as intended and pass/fail criteria were used to verify requirements.
#### 3.9. Conclusion
CollisionCheck is deemed substantially equivalent to the Predicate Device, Mobius3D (K153014) due to the similarities with the Mobius3D collision check feature. Verification and Validation testing and Hazard Analysis demonstrate that CollisionCheck is as safe and effective as the Predicate Device. The minor technological differences between CollisionCheck and the Predicate Device with regard to the shared gantry collision check feature do not raise any questions on the safety and effectiveness of the Subject Device.
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.