AI/ML, Software as a Medical Device, 3rd-Party Reviewed
Indications for Use
The AlphaPoint software is a device that allows review, analysis, and interchange of CT chest images. It is intended for use with CT Chest images to assist medical professionals in image analysis. It is not intended to be the primary interpretation. The software provides segmentation, Hounsfield numerical analysis, and substance indication. The user can review, verify and correct the results of the system and generate a report of the findings.
Device Story
AlphaPoint Imaging Software is a DICOM-compliant application framework for CT chest image analysis. It retrieves imaging studies from PACS; performs automated segmentation and Hounsfield unit measurements to identify and quantify substances (e.g., air, lung, soft tissue, fat, water, blood, bone); and generates a Preliminary Findings Report. The system is operated by medical professionals in a clinical setting. Users review, verify, and correct the system-generated results before finalizing the report, which is then sent back to PACS. The device serves as an assistive tool for image analysis rather than a primary diagnostic interpretation, aiming to streamline workflow and provide quantitative data to support clinical decision-making.
Clinical Evidence
No clinical data. Performance was established through bench testing, including verification of DICOM standard compliance and formal software validation. Validation included design reviews, code reviews, unit testing, and system testing traced to requirements. Testing was performed by independent engineers and documented in a Software Test Report (STR) confirming the software is ready for release.
Technological Characteristics
Software-based imaging processing system. Written in C++, C#, and Matlab. Integrates with PACS via DICOM. Features include automated segmentation and Hounsfield numerical analysis. Operates as a standalone application framework.
Indications for Use
Indicated for medical professionals to assist in the analysis of CT chest images. Provides segmentation and Hounsfield numerical analysis to identify substances including air, lung, soft tissue, fat, water, transudate, exudate, blood, muscle, and bone. Not intended for primary interpretation.
Regulatory Classification
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.
Special Controls
*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).
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# 510(k) Summary Prepared January 7, 2011
.
APR 1 3 2012
| Sponsor: | RadLogics, Inc. |
|-----------------------------------------------------------------------------------|----------------------------------------------------------------------------------|
| Contact Person: | Moshe Becker |
| Telephone: | 408 966 4874 |
| Fax: | 408 966 4874 |
| Submission Date: | December 6, 2011 |
| Device Name: | AlphaPoint Imaging Software |
| Common Name: | Imaging Software |
| Classification:<br>Regulatory Class:<br>Review Category:<br>Classification Panel: | II<br>Class II<br>Radiology<br>System, Imaging Processing ; 21 CFR 892.2050; LLZ |
## A. Legally Marketed Predicate Devices
The modified software is substantially equivalent to the Vitrea 2 software manufactured by Vital Images, Inc. and cleared pursuant to K060378.
## B. Device Description:
The AlphaPoint system provides a full application framework with integration to PACS using DICOM. The system has the following functions:
- Communicates with PACS to get imaging studies for processing; .
- Activates one or more applications that process the imaging data . and use segmentation and Hounsfield measurements algorithms to find and measure various attributes in the images, and also identify particular slices as references images for the findings ;
- Formats the processing results for each study into a Preliminary . Findings Report
- Sends the results to PACS. .
- The software is written in C++, C# and Matlab. .
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#### C. Intended Use
ﻨﺎ
The AlphaPoint software is a device that allows review, analysis, and interchange of CT chest images. It is intended for use with CT Chest images to assist medical professionals in image analysis. It is not intended to be the primary interpretation. The software provides segmentation, Hounsfield numerical analysis, and substance indication. The user can review, verify and correct the results of the system and generate a report of the findings.
## D. Substantial Equivalence
The submission device is substantially equivalent to the predicate software device with regard to both intended use and technological characteristics. Both devices retrieve and process chest images from Computed Tomography (CT), Both systems are DICOM compliant.
## E. Performance Data
The AlphaPoint software has been verified and validated according to the company's design control process. It was tested for compliance with the DICOM Standard and passed the six DICOM specific test cases provided in Section 4.4.2 of the Validation Test Report. The software development process complies with FDA Guidance documents related to software in Medical Devices and all of the documents specified have been submitted in the 510(k) Notification and can be summarized as follows
The software development life cycle includes various verification activities and the formal software validation. The software verification activities include design reviews, code reviews, unit tests and system testing.
The Software Test Description (STD) for the Alphapoint System describes the test cases for the device, along with its acceptance criteria, and the detailed test procedure. The test cases in the STD are traced to the requirements found in the SRS. The STD was developed using the template found in the IEEE Std 829-2008, IEEE Standard for Software Test Documentation and J-STD 016 (IEEE STD 1498), Software Development and Documentation.
The validation test runs were documented in the Software Test Report (STR) for the Alphapoint System. The STR contains the date, tester name, software versions, specific configurations used in the testing and the Pass/Fail grade for each test procedure step. The STR summarizes the validation assessment, which states that the software is ready for release. The STR was developed using the template found in the IEEE Std 829-2008, IEEE Standard for Software Test Documentation and J-STD 016 (IEEE STD 1498), Software Development and Documentation.
The validation was performed by a qualified independent software validation engineers who were not directly involved in the software design and implementation efforts. All anomalies have been resolved.
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Image /page/2/Picture/0 description: The image shows the logo for the U.S. Department of Health & Human Services. The logo consists of a stylized eagle with three lines representing its body and wings. The eagle is enclosed in a circle with the text "DEPARTMENT OF HEALTH & HUMAN SERVICES USA" around the perimeter of the circle.
# DEPARTMENT OF HEALTH & HUMAN SERVICES
Public Health Service
Food and Drug Administration 10903 New Hampshire Avenue Document Control Room - WO66-G609 Silver Spring, MD 20993-0002
RadLogics, Inc. Mr. Mark Job Accredited Third Party Reviewer % Regulatory Technology Services Incorporated 1394 25th Street, NW BUFFALO MN 55313
APR 1 3 2012
Re: K120161
Trade/Device Name: AlphaPoint Imaging Software Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: II Product Code: LLZ Dated: February 20, 2012 Received: February 21, 2012
#### Dear Mr. Job:
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.
If your device is classified (see above) into class II (Special Controls), it may be subject to such additional controls. Existing major regulations affecting your device can be found in Title 21, Code of Federal Regulations (CFR), Parts 800 to 895. 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 Parts 801 and 809); medical device reporting (reporting of
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medical device-related adverse events) (21 CFR 803); and good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820). This letter will allow you to begin marketing your device as described in your Section 510(k) premarket notification. The FDA finding of substantial equivalence of your device to a legally marketed predicate device results in a classification for your device and thus, permits your device to proceed to the market.
If you desire specific advice for your device on our labeling regulation (21 CFR Parts 801 and 809), please contact the Office of In Vitro Diagnostic Device Evaluation and Safety at (301) 796-5450. 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.
You may obtain other general information on your responsibilities under the Act from the Division of Small Manufacturers, International and Consumer Assistance at its toll-free number (800) 638-2041 or (301) 796-7100 or at its Internet address http://www.fda.gov/cdrh/industry/support/index.html.
Sincerely Yours,
Janine M. Morris
Acting Director Division of Radiological Devices Office of In Vitro Diagnostic Device Evaluation and Safety Center for Devices and Radiological Health
Enclosure
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#### 510(k) Number (if known):
Device Name:
AlphaPoint Imaging Software
Indications for Use:
The AlphaPoint software is a device that allows review, analysis, and interchange of CT chest images. It is intended for use with CT Chest images to assist medical professionals in image analysis. It is not intended to be the primary interpretation. The software provides segmentation and Hounsfield numerical analysis values which are indicative of various substances (i.e., air, lung, soft tissue, fat, water, transudate, exudate, blood, muscle and bone). The user can review, verify and correct the results of the system and generate a report of the findings. .
Prescription Use · × (Part 21 CFR 801 Subpart D)
AND/OR
Over-The-Counter Use (21 CFR 801 Subpart C)
#### (PLEASE DO NOT WRITE BELOW THIS LINE-CONTINUE ON ANOTHER PAGE OF NEEDED)
Concurrence of CDRH, Office of In Vitro Diagnostic Devices (OIVD)
(Division Sign-Off)
Division of Radiological Devices
Office of in Vitro Diagnostic Device Evaluation and Safety
510K
K120161
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Learn the FDA Browser
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.