(437 days)
Yes
The device description explicitly states that "MRI Planner utilizes pre-trained machine learning models to perform both the conversion to synthetic CT and the automated structure contouring."
No.
The device is intended for use in radiation treatment planning by providing information on tissue properties for radiation attenuation estimation and deriving contours, rather than directly treating a disease or condition.
No
The device processes images to provide information for radiation treatment planning and contoured structures, rather than directly diagnosing diseases.
Yes
The device description explicitly states "The product MRI Planner is a stand-alone software". While it requires specific hardware (x86-64 system, CUDA capable NVIDIA GPU) and an operating system (Ubuntu Linux), these are general-purpose computing platforms and not dedicated medical device hardware components included as part of the device itself. The device's functionality is entirely implemented in software.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostic devices are used to examine specimens (like blood, urine, or tissue) taken from the human body to provide information for diagnosis, monitoring, or screening.
- MRI Planner's Function: MRI Planner processes medical images (MRI scans) to assist in radiation therapy planning. It does not analyze biological specimens. Its purpose is to provide anatomical information and synthetic CT data for treatment planning, not to diagnose or monitor a patient's condition through laboratory analysis.
The device's function falls under the category of medical imaging software used for treatment planning, which is distinct from IVD devices.
No
The letter does not state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
MRI Planner is a software-only medical device intended for use by trained radiation oncologists, dosimetrists and physicists to process images from MRI systems to
-
provide the operator with information of tissue properties for radiation attenuation estimation purposes in photon external beam radiotherapy treatment planning, and to
-
derive contours for input to radiation treatment planning by assisting in localization and definition of healthy anatomical structures.
MRI Planner is not intended to automatically contour tumor clinical target volumes.
MRI Planner is indicated for radiotherapy planning of adult patients for primary and metastatic cancers in the brain and head-neck regions, as well as soft tissue cancers in the pelvic region.
MRI Planner generates synthetic OT images for radiation attenuation estimation purposes for the pelvis, brain and head-neck regions only. MRI Planner generates automatically derived contours of the bladder, colon and femoral heads, for prostate cancer patients only.
Product codes (comma separated list FDA assigned to the subject device)
MUJ, QKB
Device Description
The product MRI Planner is a stand-alone software providing information to the treatment planning process prior to radiotherapy. Based on a DICOM MR image stack, the software generates synthetic CT images that can be used for attenuation calculations in radiotherapy treatment planning for the pelvis, brain and head-neck regions. In addition, the software also generates contours of anatomical structures in the MR image stack, to be used as a starting point for the manual delineation work required in radiotherapy treatment planning. Contours are generated for prostate cancer patients only (bladder, colon and femoral heads).
MRI Planner utilizes pre-trained machine learning models to perform both the conversion to synthetic CT and the automated structure contouring. The models for synthetic CT generation was trained using a dataset comprising MR and CT images for 244 patients acquired in the treatment position at four hospitals. The model for prostate cancer patient auto contouring was trained using a dataset comprising MR images for 175 patients acquired in the treatment position at four hospitals, together with in-house generated expert manual contours. MRI Planner does not display or store DICOM images. The user is advised to use existing softwares for radiotherapy treatment planning to display and modify generated images and contours.
MRI Planner runs on a standard x86-64 compatible system with a CUDA capable NVIDIA GPU and requires Ubuntu Linux 18.04 operating system.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
MRI
Anatomical Site
pelvis, brain and head-neck regions
Indicated Patient Age Range
adult patients
Intended User / Care Setting
radiation oncologists, dosimetrists and physicists
Description of the training set, sample size, data source, and annotation protocol
The models for synthetic CT generation was trained using a dataset comprising MR and CT images for 244 patients acquired in the treatment position at four hospitals. The model for prostate cancer patient auto contouring was trained using a dataset comprising MR images for 175 patients acquired in the treatment position at four hospitals, together with in-house generated expert manual contours.
Description of the test set, sample size, data source, and annotation protocol
Dosimetric bench test data for pelvis consisted of MR (T2w) and CT images for 58 unique pelvis cancer patients acquired in the treatment position at six different hospitals. Patient distribution was 16% female and 84% male, age range 51-88 years, 41% of patient images were acquired in the US and 59% outside the US.
Dosimetric bench test data for head-neck-brain consisted of MR (T1-Dixon) and CT images for 75 unique head-neck-brain cancer patients acquired in the treatment position at four different hospitals. Patient distribution was 39% female and 64% male, age range 41-85 years, 55% of patient images were acquired in the US and 45% outside the US. MRI data was acquired at six different MRI scanner models, from two different vendors, with field strengths of 1.5T and 3T.
Auto contouring bench test data for prostate cancer patients consisted of MR (T2w) images for 51 unique male prostate cancer patients acquired in the treatment position at five different hospitals, together with manually generated delineations of bladder, colon and femoral heads. Patient age range was 51-88 years, 39% of patient images were acquired in the US and 61% outside the US.
Manual delineations were generated by two expert truthers using the consensus approach, based on US clinical guidelines. The manual delineations for the bench tests were generated at a separate time from the generation of the training dataset. The truthers were involved in the development of the product and the generation of the training dataset, but not in the training and tuning of the segmentation model. Both truthers were employed by the manufacturer at the time of performing the manual delineations for the bench test.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Dose accuracy bench test.
The conducted bench tests evaluate the dosimetric equivalence of MRI Planner generated synthetic CT (sCT) and conventional CT. Dosimetric agreement was evaluated by comparing mean doses to target and non-target volumes as well as by means of gamma evaluation. The non-target sCT-CT dose difference was evaluated by computing the mean dose difference in all sub-volumes of the dose matrix not overlapping the target. The acceptance criteria was that 99% of cases should have no sub-volumes with an sCT-CT dose difference in excess of 1.0 Gy or 5% of the CT-dose. Two variations of gamma evaluation were conducted; one focusing on the high dose area (cut off at 75% of the maximum dose) and the other considering also the medium dose regions (cut off at 25% of the maximum dose). In the high dose gamma a 3%/3mm criteria was used for the pelvic and head-neck anatomical regions, while a 2%/2mm criteria was used for brain cases. The gamma index passing rate requirement was 99% for pelvis and head-neck, and 98% for brain. In the medium dose range gamma evaluation the stricter 2%/2mm criteria was used for all anatomical regions, while requiring a 99% gamma index passing rate.
Key results:
The average sCT-CT mean target dose difference was found to be 0.02% ± 0.31% and -0.02% ± 0.25% for the anatomical regions pelvis, and head-neck and brain, respectively. No cases displayed any sub-volumes with sCT-CT dose differences in excess of 5% or 1.0 Gy. At least 95% of cases met the passing criteria across all gamma evaluations and anatomical regions. In the high dose gamma evaluation, 100.0% of cases passed the individual passing rate criterion for all anatomical regions, while the average gamma index passing rate was 99.9%, 99.8% and 99.8% for pelvis, head neck and brain, respectively. In the medium dose gamma evaluation, 98.3% and 100.0% of cases passed the individual passing rate criterion for pelvis, and head-neck and brain, respectively, while the average gamma index passing rate was 99.7%, 99.5% and 99.9% for pelvis, head neck and brain, respectively.
Sample sizes for dose accuracy bench test:
Pelvis: 58 unique pelvis cancer patients.
Head-neck-brain: 75 unique head-neck-brain cancer patients.
Auto contouring bench test.
The conducted bench tests evaluates the automatically generated prostate patient delineations (of bladder, colon and femoral heads) against manual delineations, using two common metrics; Dice score (DSC) and 95% Hausdorff distance (HD).
Key results:
The average DSC was found to be 0.95 ± 0.03, 0.90 ± 0.04 and 0.96 ± 0.01 for bladder, colon and femoral head delineations, respectively. The average 95% HD was found to be 2.69 ± 1.82, 4.96 ± 3.91 and 2.04 ± 0.49 for bladder, colon and femoral head delineations, respectively.
Sample size for auto contouring bench test:
51 unique male prostate cancer patients.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Dose agreement: Mean dose difference, Gamma index passing rate.
Contour accuracy: Dice score (DSC), 95% Hausdorff distance (HD).
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.
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
§ 892.5050 Medical charged-particle radiation therapy system.
(a)
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.(b)
Classification. Class II. When intended for use as a quality control system, the film dosimetry system (film scanning system) included as an accessory to the device described in paragraph (a) of this section, 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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Image /page/0/Picture/0 description: The image contains 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 acronym along with the full name of the agency on the right. The FDA part of the logo is in blue, with the acronym in a square and the full name, "U.S. Food & Drug Administration," written out next to it.
Spectronic Medical AB % Per Bruhn Manager of Quality Assurance and Regulatory Affairs Karbingatan 36 Helsingborg, 25467 SWEDEN
Re: K211841
Trade/Device Name: MRI Planner Regulation Number: 21 CFR 892.5050 Regulation Name: Medical charged-particle radiation therapy system Regulatory Class: Class II Product Code: MUJ, QKB Dated: July 22, 2022 Received: July 25, 2022
Dear Per Bruhn:
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
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 medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-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
Julie Sullivan, Ph.D. Assistant Director DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
Submission Number (if known)
Device Name
MRI Planner
Indications for Use (Describe)
MRI Planner is a software-only medical device intended for use by trained radiation oncologists, dosimetrists and physicists to process images from MRI systems to
-
provide the operator with information of tissue properties for radiation attenuation estimation purposes in photon external beam radiotherapy treatment planning, and to
-
derive contours for input to radiation treatment planning by assisting in localization and definition of healthy anatomical structures.
MRI Planner is not intended to automatically contour tumor clinical target volumes.
MRI Planner is indicated for radiotherapy planning of adult patients for primary and metastatic cancers in the brain and head-neck regions, as well as soft tissue cancers in the pelvic region.
MRI Planner generates synthetic OT images for radiation attenuation estimation purposes for the pelvis, brain and head-neck regions only. MRI Planner generates automatically derived contours of the bladder, colon and femoral heads, for prostate cancer patients only.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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Spectronic Medical
Image /page/3/Picture/2 description: The image shows the logo for Spectronic Medical. The word "SPECTRONIC" is in large, bold, black letters with a thick black line underneath. To the right of the word "SPECTRONIC" are three diagonal pink lines. Below the black line and to the right is the word "MEDICAL" in smaller, bold, black letters.
1. 510(k) Summary
The following summary of the present 510(k) submission is provided:
| Date Prepared | August 25, 2022 |
|---|---|
| Contact details | Spectronic Medical AB |
| Karbingatan 36 | |
| 254 67 Helsingborg | |
| SWEDEN | |
| Contact Person | Per Bruhn |
| Manager of Quality Assurance and Regulatory Affairs | |
| Spectronic Medical AB | |
| Tel: +46 735 116042 | |
| Email: per.bruhn@spectronic.se | |
| Device trade name: | |
| Common Name: | |
| Classification Regulation: | |
| Classification Name: | |
| Panel: | |
| Classification: | |
| Product Code: | MRI Planner |
| MRI Planner | |
| 21 CFR 892.5050 | |
| Medical charged-particle radiation therapy system | |
| Radiology | |
| Class II | |
| MUJ, QKB | |
| Predicate Device | Device name: MRCAT Brain |
| Manufacturer: Philips Medical Systems MR Finland | |
| 510(k) Clearance: K193109 | |
| Classification Regulation: 21 CFR 892.5050 | |
| Classification Name: Medical charged-particle radiation therapy system | |
| Classification Panel: Radiology | |
| Device Class: II | |
| Product Code: MUJ | |
| Reference Devices | Reference Device 1: |
| Device name: MRCAT Pelvis | |
| Manufacturer: Philips Medical Systems MR Finland | |
| 510(k) Clearance: K182888 | |
| Classification Regulation: 21 CFR 892.5050 | |
| Classification Name: Medical charged-particle radiation therapy system | |
| Classification Panel: Radiology | |
| Device Class: II | |
| Product Code: MUJ | |
| Reference Device 2: | |
| Device name: Limbus Contour | |
| Manufacturer: Limbus AI, Inc. | |
| 510(k) Clearance: K201232 | |
| Classification Regulation: 21 CFR 892.2050 | |
| Classification Name: Medical image management and processing system | |
| Classification Panel: Radiology | |
| Device Class: II | |
| Product Code: LLZ | |
| Device Description Summary | The product MRI Planner is a stand-alone software providing information |
| to the treatment planning process prior to radiotherapy. Based on a DICOM | |
| MR image stack, the software generates synthetic CT images that can be | |
| used for attenuation calculations in radiotherapy treatment planning for the | |
| pelvis, brain and head-neck regions. In addition, the software also | |
| generates contours of anatomical structures in the MR image stack, to be | |
| used as a starting point for the manual delineation work required in | |
| radiotherapy treatment planning. Contours are generated for prostate cancer | |
| patients only (bladder, colon and femoral heads). |
MRI Planner utilizes pre-trained machine learning models to perform both
the conversion to synthetic CT and the automated structure contouring. The
models for synthetic CT generation was trained using a dataset comprising
MR and CT images for 244 patients acquired in the treatment position at
four hospitals. The model for prostate cancer patient auto contouring was
trained using a dataset comprising MR images for 175 patients acquired in
the treatment position at four hospitals, together with in-house generated
expert manual contours. MRI Planner does not display or store DICOM
images. The user is advised to use existing softwares for radiotherapy
treatment planning to display and modify generated images and contours. |
| | MRI Planner runs on a standard x86-64 compatible system with a CUDA
capable NVIDIA GPU and requires Ubuntu Linux 18.04 operating system. |
| Indications For Use | MRI Planner is a software-only medical device intended for use by trained
radiation oncologists, dosimetrists and physicists to process images from
MRI systems to |
| | 1) provide the operator with information of tissue properties for radiation
attenuation estimation purposes in photon external beam radiotherapy
treatment planning, and to |
| | 2) derive contours for input to radiation treatment planning by assisting in
localization and definition of healthy anatomical structures. |
| | MRI Planner is not intended to automatically contour tumors or tumor
clinical target volumes. |
| | MRI Planner is indicated for radiotherapy planning of adult patients for
primary and metastatic cancers in the brain and head-neck regions, as well
as soft tissue cancers in the pelvis region. |
| | MRI Planner generates synthetic CT images for radiation attenuation
estimation purposes for the pelvis, brain and head-neck regions only. MRI
Planner generates automatically derived contours of the bladder, colon and
femoral heads, for prostate cancer patients only. |
| Indications for Use comparison | MRI Planner provides the indications for use of the Predicate Device. The
Predicate Device has the intended use to provide the operator with
information of tissue properties for radiation attenuation estimation
purposes in photon external beam radiotherapy treatment planning. |
| | MRI Planner includes the intended use of the Predicate Device, as it
provides the operator with information of tissue properties for radiation
attenuation estimation purposes in photon external beam radiotherapy
treatment planning. Additionally, it provides the functionality of auto
contouring, which is not believed to raise additional safety concerns. |
| | As such, the indications for use of MRI Planner does not raise any |
| | additional or different concerns regarding safety or effectiveness as
compared to the Predicate Device. |
| Technological comparison | Both MRI Planner and the Predicate Device are machine learning based.
Reference Device 1 was used as predicate device for the Predicate Device
and is therefore also technologically equivalent. Both the Predicate Device
and Reference Device 1 generates similar outputs as MRI Planner in the
form of synthetic CT images. Since MRI Planner has similar technological
characteristics as both the Predicate Device and Reference Device 1 with
regard to synthetic CT generation, no differences in risk have been
identified between the products. |
| | Both MRI Planner and Reference Device 2 use pre-trained machine
learning models to derive contours. Since MRI Planner has similar
technological characteristics as Reference Device 2 with regard to deriving
contours, no differences in risk have been identified between the products. |
| Summary of performance
testing | Dose accuracy bench test |
| | The conducted bench tests evaluate the dosimetric equivalence of MRI
Planner generated synthetic CT (sCT) and conventional CT. Dosimetric
agreement was evaluated by comparing mean doses to target and non-target
volumes as well as by means of gamma evaluation. The non-target sCT-CT
dose difference was evaluated by computing the mean dose difference in
all sub-volumes of the dose matrix not overlapping the target. The
acceptance criteria was that 99% of cases should have no sub-volumes with
an sCT-CT dose difference in excess of 1.0 Gy or 5% of the CT-dose. Two
variations of gamma evaluation were conducted; one focusing on the high
dose area (cut off at 75% of the maximum dose) and the other considering
also the medium dose regions (cut off at 25% of the maximum dose). In the
high dose gamma a 3%/3mm criteria was used for the pelvic and head-neck
anatomical regions, while a 2%/2mm criteria was used for brain cases. The
gamma index passing rate requirement was 99% for pelvis and head-neck,
and 98% for brain. In the medium dose range gamma evaluation the stricter
2%/2mm criteria was used for all anatomical regions, while requiring a
99% gamma index passing rate. |
| | The average sCT-CT mean target dose difference was found to be 0.02% ±
0.31% and -0.02% ± 0.25% for the anatomical regions pelvis, and head-
neck and brain, respectively. No cases displayed any sub-volumes with
sCT-CT dose differences in excess of 5% or 1.0 Gy. |
| | At least 95% of cases met the passing criteria across all gamma evaluations
and anatomical regions. In the high dose gamma evaluation, 100.0% of
cases passed the individual passing rate criterion for all anatomical regions,
while the average gamma index passing rate was 99.9%, 99.8% and 99.8%
for pelvis, head neck and brain, respectively. In the medium dose gamma
evaluation, 98.3% and 100.0% of cases passed the individual passing rate
criterion for pelvis, and head-neck and brain, respectively, while the
average gamma index passing rate was 99.7%, 99.5% and 99.9% for pelvis,
head neck and brain, respectively. |
| | Dosimetric bench test data for pelvis consisted of MR (T2w) and CT
images for 58 unique pelvis cancer patients acquired in the treatment
position at six different hospitals. Patient distribution was 16% female and
84% male, age range 51-88 years, 41% of patient images were acquired in
the US and 59% outside the US. |
| | Dosimetric bench test data for head-neck-brain consisted of MR (T1-
Dixon) and CT images for 75 unique head-neck-brain cancer patients
acquired in the treatment position at four different hospitals. Patient
distribution was 39% female and 64% male, age range 41-85 years, 55% of
patient images were acquired in the US and 45% outside the US.
MRI data was acquired at six different MRI scanner models, from two
different vendors, with field strengths of 1.5T and 3T. |
| | Auto contouring bench test |
| | The conducted bench tests evaluates the automatically generated prostate
patient delineations (of bladder, colon and femoral heads) against manual
delineations, using two common metrics; Dice score (DSC) and 95%
Hausdorff distance (HD). |
| | The average DSC was found to be 0.95 ± 0.03, 0.90 ± 0.04 and 0.96 ± 0.01
for bladder, colon and femoral head delineations, respectively. The average
95% HD was found to be 2.69 ± 1.82, 4.96 ± 3.91 and 2.04 ± 0.49 for
bladder, colon and femoral head delineations, respectively. |
| | Auto contouring bench test data for prostate cancer patients consisted of
MR (T2w) images for 51 unique male prostate cancer patients acquired in
the treatment position at five different hospitals, together with manually
generated delineations of bladder, colon and femoral heads. Patient age
range was 51-88 years, 39% of patient images were acquired in the US and
61% outside the US. |
| | Manual delineations were generated by two expert truthers using the
consensus approach, based on US clinical guidelines. The manual
delineations for the bench tests were generated at a separate time from the
generation of the training dataset. The truthers were involved in the
development of the product and the generation of the training dataset, but
not in the training and tuning of the segmentation model. Both truthers
were employed by the manufacturer at the time of performing the manual
delineations for the bench test. |
| Non-clinical test summary and
conclusion | MRI Planner complies with the following international and FDA-
recognized consensus standards: |
| | ISO 14971 Second edition 2007-03-01
Medical devices – Application of risk management to medical devices |
| | IEC 62304 Edition 1.1 2015-06
CONSOLIDATED VERSION Medical device software - Software life
cycle processes |
| | IEC 62366-1:2015
Medical devices - Part 1: Application of usability engineering to medical
devices |
| | IEC 82304-1 Edition 1.0 2016-10
Health software – Part 1: General requirements for product safety |
| | Non-clinical verification and validation tests have been performed |
| | |
| with regards to the intended use, the technical claims, the requirement
specifications and the risk management results. Non-clinical verification
and validation test results demonstrate that MRI Planner:
Complies with the aforementioned international and FDA-
recognized consensus standards Meets all acceptance criteria as described in the product test
description and is adequate for its intended use. | |
| In addition, non-clinical bench tests have been performed to investigate the
performance of MRI Planner against the Predicate Device. The bench tests
demonstrate that MRI Planner performs comparably to the Predicate
Device. | |
| Taking all of the above into account, Spectronic Medical believes that MRI
Planner is as safe and effective as the Predicate Device, and is therefore
substantially equivalent. | |
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Image /page/4/Picture/2 description: The image shows the logo for Spectronic Medical. The word "SPECTRONIC" is in large, bold, black letters with a black line underneath. Below the line, the word "MEDICAL" is in smaller, bold, black letters. There are three red diagonal lines above the word "SPECTRONIC".
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Image /page/5/Picture/2 description: The image shows the logo for Spectronic Medical. The word "SPECTRONIC" is in large, bold, black letters with a black line underneath. To the right of the word, there are three red diagonal lines, and below the right side of the word, the word "MEDICAL" is in smaller, black letters.
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Image /page/6/Picture/2 description: The image shows the logo for Spectronic Medical. The word "SPECTRONIC" is in large, bold, black letters. Above the word, there are three diagonal pink lines. Below the word, there are three diagonal black lines, and the word "MEDICAL" is in smaller, bold, black letters.
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Image /page/7/Picture/2 description: The image shows the logo for Spectronic Medical. The word "SPECTRONIC" is in large, bold, black letters. Above the word, there are three diagonal pink lines. Below the word, there are three diagonal black lines, and to the right of those lines is the word "MEDICAL" in smaller, bold, black letters.