In-vivo clinical patient images (chest PA radiographs)
Clinical images were used to validate the performance of an upgraded image post-processing engine (noise reduction algorithm) at a 50% radiation dose reduction compared to the predicate device.
Image quality assessment based on seven anatomical landmarks and diagnostic confidence
Indications for Use
The GC85A Digital X-ray Imaging System is intended for use in generating radiographic images of human anatomy by a qualified/trained doctor or technician. This device is not intended for mammographic applications.
Device Story
The GC85A is a stationary digital X-ray imaging system used in clinical settings by physicians or technicians. It captures X-ray projections through a patient's body, which are converted into electrical signals by a detector. These signals undergo amplification and digital conversion before being processed by the S-Station software. The system features an upgraded Image Post-processing Engine utilizing an advanced noise reduction algorithm to optimize image quality based on noise distribution and structural information. This allows for a 50% radiation dose reduction for routine PA chest radiography compared to the predicate device. Processed images are saved in DICOM format and transmitted to a PACS server for clinical review. By maintaining diagnostic image quality at lower radiation levels, the device aims to reduce patient radiation exposure during chest imaging.
Clinical Evidence
Clinical evaluation included 78 in-vivo chest PA image datasets (BMI 15-33) evaluated by three radiologists. Seven anatomical landmarks were assessed, including 18 cases of lung lesions (consolidations, nodules, interstitial markings). Results demonstrated that images acquired at 50% radiation dose reduction using the new engine were substantially equivalent to the predicate device in diagnostic confidence. Phantom studies using the BRH method and SNR/CNR measurements further supported image quality equivalence.
Technological Characteristics
Stationary digital X-ray system. Hardware is identical to predicate K160997. Features an upgraded software-based Image Post-processing Engine with advanced noise reduction. Connectivity via DICOM to PACS. Complies with ES 60601-1, IEC 60601-1-2, IEC 60601-1-3, IEC 60601-2-28, IEC 60601-2-54, ISO 14971, 21 CFR 1020.30, and 21 CFR 1020.31.
Indications for Use
Indicated for generating radiographic images of human anatomy in adult patients. Not for mammographic applications.
Regulatory Classification
Identification
A stationary x-ray system is a permanently installed diagnostic system intended to generate and control x-rays for examination of various anatomical regions. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.
Special Controls
*Classification.* Class II (special controls). A radiographic contrast tray or radiology diagnostic kit intended for use with a stationary x-ray system only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.
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November 22, 2017
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Samsung Electronics Co.,Ltd. % Jaesang NOH Regulatory Affairs 129, Samsung-ro, Yeongtong-gu Suwon-si. Gyeonggi-do. 16677 REPUBLIC OF KOREA
Re: K172229
Trade/Device Name: GC85A Regulation Number: 21 CFR 892.1680 Regulation Name: Stationary x-ray system Regulatory Class: II Product Code: KPR Dated: October 19, 2017 Received: October 20, 2017
Dear Jaesang NOH:
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,
Michael D. O'Hara
For
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)
K172229
Device Name GC85A
Indications for Use (Describe)
The GC85A Digital X-ray Imaging System is intended for use in generating radiographic images of human anatomy by a qualified/trained doctor or technician. This device is not intended for mammographic applications.
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Form Approved: OMB No. 0910-0120
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510(k) Premarket Notification - Traditional
## Section 5: 510(k) Summary
This summary of 510(k) safety and effectiveness information is being submitted accordance with requirements of 21 CFR 807.92
- 1. Date: July 20, 2017
### 2. Submitter
- Company Name: SAMSUNG ELECTRONICS Co., Ltd. A.
- Address: 129, Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16677, B. Republic of Korea
### 3. Primary Contact Person
- A. Name: NOH, Jaesang
- B. Title: Regulatory Affairs
- Phone Number: +82-31-200-1764 ﻥ
- FAX Number: +82-31-200-6401 D. E-Mail: jaesang.noh@samsung.com
### 4. Secondary Contact Person
- A. Name: Ninad Gujar
- B. Title: Regulatory Affairs Manager
- C. Phone Number: 978-564-8503
- FAX Number: 978-750-6677 D. E-Mail: ngujar@samsungneurologica.com
## 5. Proposed Device
- A. Trade Name: GC85A
- B. Device Name: GC85A
- C. Common Name: Digital Diagnostic X-ray System
- D. Classification Name: Stationary X-ray System
- Product Code: KPR ட்
- ட் Regulation: 21 CFR 892.1680
### 6. Predicate Devices
| Predicate Devices | |
|-------------------------|----------------------------|
| | Predicate Device |
| Device Name | GC85A |
| Classification<br>Name | Stationary X-ray<br>System |
| Product Code | KPR |
| Regulation | 21 CFR 892.1680 |
| 510(K)# | K160997 |
| 510(K)<br>Decision Date | July 6, 2016 |
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510(k) Premarket Notification - Traditional
#### 7. Device Description
The GC85A digital X-ray imaging system is used to capture images by transmitting X-ray to a patient's body. The X-ray passing through a patient's body is sent to the detector and then converted into electrical signals. These signals go through the process of amplification and digital data conversion in the signal process device before being sent to the S-Station (Operation Software) and saved in DICOM file, a standard for medical imaging. The captured images are sent to the Picture Archiving & Communication System (PACS) server, and can be used for reading images.
The Image Post-processing Engine is exclusively installed in S-station, which is a Samsung Digital X-ray Operation Software for Samsung Digital X-ray System. It has an image processing algorithm to improve an acquired image and previously cleared with K160997.
The proposed Image Post-processing Engine is upgraded with employing an advanced noise reduction algorithm to improve image quality. The proposed Engine is shown of a post-processed image as substantially equivalent as the image by the predicate Image Post-processing Engine at a certain low dose level.
This submission is intended to get 510(k) clearance for GC85A with the proposed engine by which the substantially equivalent PA radiograph for average adult chest can be taken using the 50% dose reduction for marketing purpose.
This claim is based on a limited study of an anthropomorphic phantom that simulates the x-ray properties of an average size adult, and on a small clinical study at one facility. Only routine PA chest radiography was studied, and results for larger-size adults (body mass index) greater than 30 was not studied to statistical significance. The new GC85A also was not studied with pediatric patients. The clinical site is responsible for determining whether the particular radiographic imaging needs are not impacted by such x-ray dose reduction.
#### 8. Intended Use
The GC85A Digital X-ray Imaging System is intended for use in generating radiographic images of human anatomy by a qualified/trained doctor or technician. This device is not intended for mammographic applications.
#### 9. Summary of Technological characteristic of the proposed device compared with the predicate device
The proposed device, GC85A has the same hardware characteristics as the predicate device (K160997) and includes an upgraded Image Post-processing engine. It does not have significant changes in materials, energy source or technological characteristics compared to the predicate device.
#### A. Comparing with Predicate Device
| | Specification | Predicate Device | Proposed Device | Discussion |
|--|---------------|------------------|-----------------|------------|
| | Device Name | GC85A | GC85A | - |
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510(k) Premarket Notification - Traditional
| Manufacturer | SAMSUNG ELECTRONICS<br>co., ltd. | SAMSUNG ELECTRONICS<br>co., ltd. | - |
|---------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
| 510(k)<br>Number | K160997 | None | - |
| Intended Use | The GC85A digital X-ray<br>imaging system is intended<br>for use in generating<br>radiographic images of<br>human anatomy by a<br>qualified/trained doctor or<br>technician. This device is not<br>intended for mammographic<br>applications. | The GC85A Digital X-ray<br>Imaging System is intended<br>for use in generating<br>radiographic images of<br>human anatomy by a<br>qualified/trained doctor or<br>technician. This device is not<br>intended for mammographic<br>applications. | Same |
| Image Post-Processing engine | | | |
| Noise<br>reduction<br>algorithm | Image Post-processing<br>Engine with a conventional<br>noise reduction algorithm | Image Post-processing<br>Engine with an advanced<br>noise reduction algorithm | Different(1) |
| No | Differences | Explanation | |
| (1) | Image Post-processing Engine with an advanced noise reduction algorithm | The Image Post-processing Engine is upgraded with employing an advanced noise reduction algorithm which optimizes the degree of noise reduction for improvement of image quality according to the noise distribution and the structural information of a digital radiographic image.<br>While the upgraded Image Post-processing Engine is shown a substantially equivalent image and safe as same as the predicate device's one, it can be taken by the 50% dose reduction comparing with the predicate Image Post-processing Engine. | |
#### 10. Safety, EMC and Performance Data
Electrical, mechanical, environmental safety and performance testing according to standard ES 60601-1,IEC 60601-1-2, IEC 60601-1-3, IEC 60601-2-28, IEC 60601-2-54, ISO14971, 21CFR1020.30 and 21CFR1020.31 were performed, and EMC testing was conducted in accordance with standard IEC 60601-1-2. Wireless function was tested and verified followed by guidance, Radio frequency Wireless Technology in Medical Devices. All test results were satisfying the standards.
#### 11. Non-clinical data
Non-clinical testing data was provided in conformance to the FDA "Guidance for the Submission of 510(k)'s for Solid-State X-ray Imaging Devices", which includes MTF and DQE measurements as tested by IEC 62220-1.
The proposed Image Post-processing Engine was evaluated with a semi-anatomical chest phantom at a various radiation dose. SNR and CNR were measured to determine image quality with the images taken using the proposed engine or the predicated engine. Overall images using the proposed engine make it easy to distinguish between background and the object and is clearer than images using the predicated engine. The
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#### 510(k) Premarket Notification - Traditional
qualitative side including usefulness was validated by a radiographer using clinical images which was proved in 'Clinical data' section.
#### 12. Clinical data
Phantom image evaluations were performed in accordance with FDA guidance for the submission of 510(k)'s for Solid State X-ray Imaging Devices. They were evaluated by professional radiologists and found to be equivalent to the predicate device.
The proposed engine was validated with anthropomorphic chest phantom images and clinical images by three professional radiologists. Anthropomorphic chest phantom images were scored by Bureau of Radiological Health (BRH) method and inter-observer agreement was calculated. Clinical image evaluations were performed to support phantom study results under the patient informed consent. Total 78 in-vivo data set of chest PA images were evaluated by three experienced radiologists, covering subject's BMI from 15 to 33. Seven anatomical landmarks were evaluated for image quality assessment by three readers, including 18 cases of featured lung lesions of chest PA images, consolidations, nodules and interstitial markings. The images using the proposed engine taken at 50% reduction in radiation dose are substantially equivalent to those using the predicated engine without sacrificing diagnostic confidence.
#### 13. Conclusions
The non-clinical and clinical data demonstrate that the proposed device is as safe, as effective, and performs as well as the legally marketed device.
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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.