(245 days)
Yes
The document explicitly states that the device is a "data driven image reconstruction method based on deep learning technology" and utilizes "dedicated deep neural networks".
No.
The device is an image reconstruction method for CT scans, improving image quality and reducing dose, which aids in diagnosis. It does not directly treat or prevent a medical condition.
No
Explanation: Deep Recon is an image reconstruction method for CT scans, aimed at improving image quality (reducing noise, improving low contrast detectability) and potentially reducing radiation dose. It does not provide a diagnosis itself; it generates images that can then be used by a medical professional for diagnostic purposes. The text explicitly states, "Deep Recon is intended to produce cross-sectional images by computer reconstruction..." and "...can reduce the dose required for diagnostic CT imaging," implying it's a tool for diagnostic imaging, not a diagnostic device itself.
No
The device is described as a "data driven image reconstruction method" that is "integrated on the mentioned CT systems as a part of reconstruction chain." While it is software, it is explicitly stated to be a component of the uCT 760 and uCT 780 CT systems, which are hardware devices. Therefore, it is not a standalone software-only medical device.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze biological samples: IVDs are designed to examine specimens taken from the human body, such as blood, urine, tissue, etc., to provide information about a person's health.
- This device processes imaging data: Deep Recon processes X-ray transmission data to reconstruct CT images. It does not analyze biological samples.
- Its intended use is image reconstruction: The intended use clearly states that it is for producing cross-sectional images from X-ray data.
- The device description focuses on image processing: The description details how it uses deep learning to improve image quality (noise reduction, low contrast detectability).
Therefore, Deep Recon falls under the category of medical imaging software or a component of a medical imaging system, not an In Vitro Diagnostic device.
No
The input text does not contain any explicit statement that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
Deep Recon is a data driven image reconstruction method based on deep learning technology. It is intended to produce cross-sectional images by computer reconstruction of X-ray transmission data taken at different angles planes, including Axial, Helical, and Cardiac acquisition.
Deep Recon is designed to generate CT images with lower image noise, and improved low contrast detectability, and it can reduce the dose required for diagnostic CT imaging.
Deep Recon can be used for head, chest, abdomen, cardiac and vascular CT applications for adults. Deep Recon is intended to be used with uCT 760 and uCT 780 only.
Product codes (comma separated list FDA assigned to the subject device)
JAK
Device Description
The Deep Recon is a data driven image reconstruction method based on deep learning technology. Dedicated deep neural networks are designed and trained for different body parts. As a part of reconstruction chain, the Deep Recon generates CT images with an appearance similar to traditional FBP, but with a decreased image noise, and an improved low contrast detectability. The Deep Recon was specifically trained on uCT 760 and uCT 780 (K172135). The function is integrated on the mentioned CT systems as a part of reconstruction chain.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Deep Recon is a data driven image reconstruction method based on deep learning technology.
Dedicated deep neural networks are designed and trained for different body parts.
Utilizes a dedicated Deep Neural Network (DNN) which is trained on the CT Scanner and designed specifically to generate high quality CT images
Dedicated deep neural network (DNN) which is trained on low dose FBP images to get normal dose (high quality) FBP images
Input Imaging Modality
X-ray transmission data / CT
Anatomical Site
head, chest, abdomen, cardiac and vascular
Indicated Patient Age Range
adults
Intended User / Care Setting
Not Found
Description of the training set, sample size, data source, and annotation protocol
Dedicated deep neural networks are designed and trained for different body parts. The Deep Recon was specifically trained on uCT 760 and uCT 780 (K172135).
Description of the test set, sample size, data source, and annotation protocol
A total of 80 retrospectively collected clinical cases. The raw data from each of these cases was reconstructed with both Filtered Back Projection and Deep Recon. Each image was read by 2 board-certified radiologists who provided an assessment of both image noise and structure fidelity according to a 4point scale (1=unacceptable for diagnostic interpretation, 2=suboptimal, acceptable for limited diagnostic information only, 3=average, acceptable for diagnostic interpretation, 4=better than usual, acceptable for diagnostic interpretation).
An additional study used a total of 40 retrospectively collected clinical cases (20 low dose cases and 20 standard dose cases). Each of the low dose cases was reconstructed with Deep Recon and compared with standard dose case reconstructed with Filtered Back Projection. Each image was read by a board-certified radiologist who provided an assessment of both image quality and clinical features according to a 5-point scale (1 = Unacceptable for diagnostic interpretation, 2 = Suboptimal, acceptable for limited diagnostic information only, 3 = Average, acceptable for diagnostic interpretation, 4 = Better than usual acceptable for diagnostic interpretation, 5 = Excellent for diagnostic interpretation). A comment about image quality and clinical features also left.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Non-Clinical Testing:
Engineering bench testing was performed. The evaluation and analysis used the same raw datasets obtained on UIH's uCT 760/780 and then applies both Deep Recon and Filtered Back Projection reconstruction. The resultant images were then compared for: Low contrast detectability (LCD) using the CCT189 MITA CT IQ low contrast phantom; Image noise using the CCT189 MITA CT IQ low contrast phantom; Mean CT number and uniformity using uniform water phantoms; Spatial resolution using the Catphan 700 phantom; Reconstructed section thickness using the Catphan 700 phantom.
Bench testing shows that the Deep Recon provides equivalent or better performance (improved LCD, decreased image noise, equivalent uniformity/spatial resolution/ reconstructed section thickness) compared to Filtered Back Projection.
Clinical Image Evaluation (Reader Study 1):
Study type: Reader study using retrospectively collected clinical cases.
Sample size: 80 cases.
Key results: The results of the study indicate that Deep Recon is equivalent or better than Filtered Back Projection in diagnostic quality.
Clinical Image Evaluation (Reader Study 2):
Study type: Reader study using retrospectively collected clinical cases.
Sample size: 40 cases (20 low dose, 20 standard dose).
Key results: The result of the study indicate that low dose images with Deep Recon are equivalent or better than standard dose images with Filtered Back Projection in diagnostic quality.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Not Found
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is a blue square with the letters "FDA" in white, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Shanghai United Imaging Healthcare Co., Ltd. % Shumei Wang QM & RA VP No. 2258 Chengbei Road Shanghai, Shanghai 201807 CHINA
Re: K193073
Trade/Device Name: Deep Recon Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray system Regulatory Class: Class II Product Code: JAK Dated: May 25, 2020 Received: May 27, 2020
Dear Shumei Wang:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for
July 6, 2020
1
devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely.
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
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Indications for Use
510(k) Number (if known) K193073
Device Name Deep Recon
Indications for Use (Describe)
Deep Recon is a data driven image reconstruction method based on deep learning technology. It is intended to produce cross-sectional images by computer reconstruction of X-ray transmission data taken at different angles planes, including Axial, Helical, and Cardiac acquisition.
Deep Recon is designed to generate CT images with lower image noise, and improved low contrast detectability, and it can reduce the dose required for diagnostic CT imaging.
Deep Recon can be used for head, chest, abdomen, cardiac and vascular CT applications for adults. Deep Recon is intended to be used with uCT 760 and uCT 780 only.
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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Image /page/3/Picture/1 description: The image contains the logo for United Imaging. The logo consists of the word "UNITED" stacked on top of the word "IMAGING" in a bold, sans-serif font. To the right of the words is a stylized "U" shape, which is also in a bold font. The color of the text and the "U" shape is a dark teal.
510 (k) SUMMARY
-
- Date of Preparation May 25, 2020
2. Sponsor Identification
Shanghai United Imaging Healthcare Co.,Ltd.
No.2258 Chengbei Rd. Jiading District, 201807, Shanghai, China
Contact Person: Shumei Wang Position: QM&RA VP Tel: +86-021-67076888-6776 Fax: +86-021-67076889 Email: shumei.wang(@united-imaging.com
3. Identification of Proposed Device
Trade Name: Deep Recon Common Name: Computed Tomography X-ray System Model(s): Deep Recon
Regulatory Information Regulation Number: 21 CFR 892.1750 Regulation Name: Computed Tomography X-ray System Regulatory Class: II Product Code: JAK Review Panel: Radiology
4. Identification of Predicate Device(s)
Primary Predicate Device:
510(k) Number: K172135 Device Name: uCT Computed Tomography X-Ray System Model(s): uCT 760, uCT 780
Regulatory Information Regulation Number: 21 CFR 892.1750 Regulation Name: Computed Tomography X-ray System Regulatory Class: II Product Code: JAK Review Panel: Radiology
Secondary Predicate Device:
510(k) Number: K183202 Device Name: Deep Learning Image Reconstruction
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Image /page/4/Picture/1 description: The image contains the logo for United Imaging. The text "UNITED" is stacked on top of the text "IMAGING". To the right of the text is a stylized letter "U" that is dark gray. The logo is simple and modern.
Regulatory Information Regulation Number: 21 CFR 892.1750 Regulation Name: Computed Tomography X-Ray System Regulatory Class: II Product Code: JAK Review Panel: Radiology
5. Device Description:
The Deep Recon is a data driven image reconstruction method based on deep learning technology. Dedicated deep neural networks are designed and trained for different body parts. As a part of reconstruction chain, the Deep Recon generates CT images with an appearance similar to traditional FBP, but with a decreased image noise, and an improved low contrast detectability. The Deep Recon was specifically trained on uCT 760 and uCT 780 (K172135). The function is integrated on the mentioned CT systems as a part of reconstruction chain.
6. Indications for Use
Deep Recon is a data driven image reconstruction method based on deep learning technology. It is intended to produce cross-sectional images by computer reconstruction of X-ray transmission data taken at different angles planes, including Axial, Helical, and Cardiac acquisition.
Deep Recon is designed to generate CT images with lower image noise, and improved low contrast detectability, and it can reduce the dose required for diagnostic CT imaging.
Deep Recon can be used for head, chest, abdomen, cardiac and vascular CT applications for adults.
Deep Recon is intended to be used with uCT 760 and uCT 780 only.
| Specification/
| Attribute | Primary Predicate Device | Secondary Predicate Device | Proposed Device |
|---|---|---|---|
| Filtered Back Projection | |||
| (FBP) on uCT 760/780 | |||
| (K172135) | Deep Learning Image | ||
| Reconstruction (K183202) | Deep Recon | ||
| Technology | Basic analytic | ||
| reconstruction method | Utilizes a dedicated Deep | ||
| Neural Network (DNN) | |||
| which is trained on the CT | |||
| Scanner and designed | |||
| specifically to generate | |||
| high quality CT images | Dedicated deep neural | ||
| network (DNN) which is | |||
| trained on low dose FBP | |||
| images to get normal | |||
| dose (high quality) FBP | |||
| images | |||
| Clinical | |||
| Workflow | Select recon type and | ||
| convolution kernel | Select recon type and | ||
| strength | Select recon type, | ||
| convolution kernel and | |||
| strength (noise index | |||
| level) |
7. Comparison of Technological Characteristics with the Predicate Devices
Deep Recon utilizes the same hardware with the primary predicate device and does not introduce any new restrictions on use.
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Image /page/5/Picture/1 description: The image contains the logo for United Imaging. The words "UNITED" and "IMAGING" are stacked on top of each other in a bold, sans-serif font. To the right of the text is a stylized "U" shape, which is divided vertically by a white line. The logo is simple and modern, with a focus on the company name.
The technological characteristics of Deep Recon is substantially equivalent to the secondary predicate device Deep Learning Image Reconstruction, the differences do not affect the safety and effectiveness.
8. Performance Data
Non-Clinical Testing
Non-clinical testing including image performance tests and clinical image evaluation were conducted for the Deep Recon during the product development. UNITED IMAGING HEALTHCARE claims conformance to the following standards and guidance:
Software
- A NEMA PS 3.1-3.20(2011): Digital Imaging and Communications in Medicine (DICOM)
- A IEC 62304: Medical Device Software - software life cycle process
- Guidance for the Content of Premarket Submissions for Software Contained in A Medical Devices
- A Content of Premarket Submissions for Management of Cybersecurity in Medical Devices
Other Standards and Guidance
- ISO 14971: Medical Devices Application of risk management to medical A devices
- Code of Federal Regulations, Title 21, Part 820 Quality System Regulation A
- Code of Federal Regulations, Title 21, Subchapter J Radiological Health A
Software Verification and Validation
Software documentation for a Moderate Level of Concern software per FDA' Guidance Document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" is included as a part of this submission.
The risk analysis was completed and risk control was implemented to mitigate identified hazards. The testing results show that all the software specifications have met the acceptance criteria. Verification and validation testing of the proposed device was found acceptable to support the claim of substantial equivalence.
UNITED IMAGING HEALTHCARE conforms to the Cybersecurity requirements by implementing a process of preventing unauthorized access, modification, misuse or denial of use, or unauthorized use of information that is stored, accessed, or transferred from a medical device to an external recipient. Cybersecurity information in accordance with guidance document "Content of Premarket
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Image /page/6/Picture/1 description: The image contains the logo for United Imaging. The logo consists of the words "UNITED IMAGING" in bold, sans-serif font, stacked on top of each other. To the right of the text is a stylized "U" symbol, which is dark gray and has a white vertical line running through the center, creating a negative space effect.
Submissions for Management of Cybersecurity in Medical Devices" is included in this submission.
Performance Verification
Engineering bench testing was performed to support substantial equivalence and the product performance claims. The evaluation and analysis used the same raw datasets obtained on UIH's uCT 760/780 and then applies both Deep Recon and Filtered Back Projection reconstruction. The resultant images were then compared for:
-
Low contrast detectability (LCD) using the CCT189 MITA CT IQ low contrast phantom (The Phantom Laboratory, Salem, NY) and a model observer
-
Image noise using the CCT189 MITA CT IQ low contrast phantom
-
Mean CT number and uniformity using uniform water phantoms
-
Spatial resolution using the Catphan 700 phantom (The Phantom Laboratory, Salem, NY) with a small diameter tungsten wire inside to generate the point spread function
-
Reconstructed section thickness using the Catphan 700 phantom with a pair of tungsten ramps
Bench testing shows that the Deep Recon provides equivalent or better performance (improved LCD, decreased image noise, equivalent uniformity/spatial resolution/ reconstructed section thickness) compared to Filtered Back Projection.
Clinical Image Evaluation
The reader study used a total of 80 retrospectively collected clinical cases. The raw data from each of these cases was reconstructed with both Filtered Back Projection and Deep Recon. Each image was read by 2 board-certified radiologists who provided an assessment of both image noise and structure fidelity according to a 4point scale (1=unacceptable for diagnostic interpretation, 2=suboptimal, acceptable for limited diagnostic information only, 3=average, acceptable for diagnostic interpretation, 4=better than usual, acceptable for diagnostic interpretation). The results of the study indicate that Deep Recon is equivalent or better than Filtered Back Projection in diagnostic quality.
An additional study used a total of 40 retrospectively collected clinical cases (20 low dose cases and 20 standard dose cases). Each of the low dose cases was reconstructed with Deep Recon and compared with standard dose case reconstructed with Filtered Back Projection. Each image was read by a board-
7
Image /page/7/Picture/1 description: The image contains the logo for United Imaging. The words "UNITED IMAGING" are stacked on top of each other in a bold, sans-serif font. To the right of the text is a stylized "U" shape, which is also in a bold font. The logo is simple and modern, and the colors are muted.
certified radiologist who provided an assessment of both image quality and clinical features according to a 5-point scale (1 = Unacceptable for diagnostic interpretation, 2 = Suboptimal, acceptable for limited diagnostic information only, 3 = Average, acceptable for diagnostic interpretation, 4 = Better than usual acceptable for diagnostic interpretation, 5 = Excellent for diagnostic interpretation). A comment about image quality and clinical features also left. The result of the study indicate that low dose images with Deep Recon are equivalent or better than standard dose images with Filtered Back Projection in diagnostic quality.
Clinical Testing
No Clinical Study is included in this submission.
9. Conclusions
The changes associated with Deep Recon do not change the indications for use from the primary predicate device, with no impact on control mechanism, operating principle, and energy type. Deep Recon also represents equivalent technological characteristic to the secondary predicate device.
Deep Recon was developed under UIH's quality management system. Design verification, along with bench testing and the clinical reader study demonstrate that Deep Recon is substantially equivalent and as safe and as effective as the legally marketed predicate device.
Based on the comparison and analysis above, the proposed device has similar performance, equivalent safety and effeteness as the predicate device. The differences above between the proposed device and predicate device do not affect the intended use, safety and effectiveness. And no issues are raised regarding to safety and effectiveness. The proposed device is determined to be Substantially Equivalent (SE) to the predicate device.