(98 days)
V8/XV8/XH8, V7/XV7/XH7, V6/XV6/XH6 Diagnostic Ultrasound System (K240631)
Yes
The document explicitly mentions several features (BiometryAssist, HeartAssist, ViewAssist, EzVolume, Live ViewAssist, UterineContour) that are "based on AI technology" and describes validation using "deep learning based" algorithms for tasks like quality assessment, segmentation, and view recognition.
No.
The device is described as a "diagnostic ultrasound system" and its intended use is for "clinical diagnosis of patients," specifically to "obtain ultrasound images and analyze body fluids." There is no mention of treating or curing any conditions.
Yes
The device is explicitly referred to as a "diagnostic ultrasound system" multiple times in the "Intended Use / Indications for Use" and "Device Description" sections, and its purpose is stated as obtaining images and analyzing body fluids for "clinical diagnosis of patients".
No
The device is described as a "diagnostic ultrasound system and probes" and a "general purpose, mobile, software controlled, diagnostic ultrasound system." While it is software-controlled and includes AI features, it explicitly mentions hardware components (system and probes) that are essential for its function of acquiring ultrasound data.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVD Definition: In Vitro Diagnostics are medical devices used to perform tests on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections. The tests are performed outside the body (in vitro).
- Device Function: The description clearly states that this is a "diagnostic ultrasound system and probes." Ultrasound is an imaging modality that uses sound waves to create images of internal body structures. This is an in vivo (within the living body) diagnostic method, not an in vitro one.
- Intended Use: The intended use describes obtaining ultrasound images and analyzing body fluids. While it mentions analyzing body fluids, the primary function and the detailed clinical applications listed (Fetal/Obstetrics, Abdominal, Cardiac, etc.) are all based on ultrasound imaging of internal anatomy. The "analyze body fluids" part is likely a secondary function or capability related to the ultrasound imaging itself (e.g., analyzing fluid accumulation or flow within the body), not performing laboratory tests on collected samples.
- Device Description: The device description focuses entirely on the ultrasound system's capabilities for acquiring and displaying ultrasound data in various modes.
- AI Features: The AI features described (BiometryAssist, HeartAssist, ViewAssist, EzVolume, UterineContour, Live ViewAssist) are all related to assisting with the interpretation, measurement, and analysis of ultrasound images, not with analyzing samples outside the body.
Therefore, while it is a diagnostic medical device, it falls under the category of in vivo diagnostic imaging systems, not In Vitro Diagnostics.
No
The letter does not state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
The diagnostic ultrasound system and probes are designed to obtain ultrasound images and analyze body fluids.
The clinical applications include: Fetal/Obstetrics, Abdominal, Gynecology, Intra-operative, Pediatric, Small Organ, Neonatal Cephalic, Adult Cephalic, Trans-vaginal, Muscular-Skeletal (Conventional, Superficial), Urology, Cardiac Adult, Cardiac Pediatric, Thoracic, Trans-esophageal (Cardiac) and Peripheral vessel.
It is intended for use by, or by the order of, and under the supervision of, an appropriately trained healthcare professional who is qualified for direct use of medical devices. It can be used in hospitals, private practices, clinics and similar care environment for clinical diagnosis of patients.
Modes of Operation: 2D mode. Color Doppler mode. Pulsed Wave (PW) Doppler mode. Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TDI) mode, Tissue Doppler Wave (TDW) mode, Power Doppler (PD) mode, ElastoScan™ Mode, MV-Flow Mode, Multi Image mode (Dual Quad) Combined modes 3D/AD model
Product codes
IYN, IYO, ITX, QIH, LLZ
Device Description
The HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system are a general purpose, mobile, software controlled, diagnostic ultrasound system. Their function is to acquire ultrasound data and to display the data as 2D mode, Color Doppler mode, Power Doppler (PD) mode, M mode, Pulsed Wave (PW) Doppler mode, Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TDI) mode, Tissue Doppler Wave (TDW) mode, ElastoScan Mode, Combined modes, MV-Flow mode, Multi-Image mode(Dual, Quad), 3D/4D mode.
The HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system also give the operator the ability to measure anatomical structures and offer analysis packages that provide information that is used to make a diagnosis by competent health care professionals. The HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system have a real time acoustic output display with two basic indices, a mechanical index and a thermal index, which are both automatically displayed.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Ultrasound
Anatomical Site
Fetal/Obstetrics, Abdominal, Gynecology, Intra-operative, Pediatric, Small Organ, Neonatal Cephalic, Adult Cephalic, Trans-vaginal, Muscular-Skeletal (Conventional, Superficial), Urology, Cardiac Adult, Cardiac Pediatric, Thoracic, Trans-esophageal (Cardiac) and Peripheral vessel. (Specific anatomical sites mentioned in AI feature descriptions: profile, bladder, spine, 4CV (4 Chamber View), LVOT (Left Ventricular Outflow Tract), RVOT (Right Ventricular Outflow Tract), Kidney (Axial, Sagittal, Coronal), MSP (Midsagittal Plane), Fetus, Head, Body, Limbs, Fluid, Umbilical-cord, Placenta, Uterus, Endometrium, Fetal Heart)
Indicated Patient Age Range
Not Found (Specific patient categories: Neonatal Cephalic, Adult Cephalic, Cardiac Adult, Cardiac Pediatric, Fetal, Reproductive age)
Intended User / Care Setting
It is intended for use by, or by the order of, and under the supervision of, an appropriately trained healthcare professional who is qualified for direct use of medical devices. It can be used in hospitals, private practices, clinics and similar care environment for clinical diagnosis of patients.
Description of the training set, sample size, data source, and annotation protocol
Live ViewAssist:
- Data set with SAMSUNG MEDISON's ultrasound systems.
- Mix of data from retrospective data collection and prospective data collection in clinical practice.
- All acquired images for training, tuning and validation were first classified into the acceptable and not-acceptable views by three participating experts. Afterwards, corresponding anatomy areas were manually drawn for each of the image. The participating experts were composed of an obstetrician with more than 20 years of experience and two sonographers with more than 10 years of experience, all in fetal cardiology. The entire process was supervised by another obstetrician with more than 25 years of experience.
- Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the three.
EzVolume:
- Fetal ultrasound 3D volumes were collected and divided into the 1st and 2nd/3rd trimesters of pregnancy.
- Data set acquired with SAMSUNG MEDISON's ultrasound.
- Mix of data from retrospective data collection and prospective data collection in clinical practice.
- All GTs (ground truths) of target region for training, validation and evaluation were drawn manually by four participating clinical experts. The participating experts were composed of an obstetrician with more than 20 years of experience and three examiners with more than 10 years of experience, all in fetal diagnosis. The entire process was supervised by another obstetrician with more than 25 years of experience.
- Data used for training, validation and evaluation purpose are completely separated from the ones during training process, and there is no overlap among the three.
UterineContour:
- 450 sagittal uterus images collected at three hospitals for segmentation test.
- 30 sagittal images of uterus collected at three hospitals for 3D coronal view adaptation test.
- Data set acquired with SAMSUNG MEDISON's ultrasound systems and probes.
- Mix of data from retrospective data collection and prospective data collection in clinical practice.
- Segmentation of the ground truth was generated by three participating OB/GYN experts with more than 10 years' experience. Each expert delineated uterus and endometrium in sagittal and uterus in transverse images regarding all of the ground truth dataset respectively. The GT results of the 3 experts did not match was reviewed in three participating OB/GYN experts together and fixed the wrong part with consensus.
- Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the data.
ViewAssist:
- 1600 fetal ultrasound images and fetal biometry images collected at two hospitals.
- Data set acquired with SAMSUNG MEDISON's ultrasound systems.
- Mix of data from retrospective data collection and prospective data collection in clinical practice.
- All acquired images for training, tuning and validation were first classified into the correct views by three participating experts. Afterwards, corresponding anatomy areas were manually drawn for each of the image. The participating experts were composed of an obstetrician with more than 20 years of experience and two sonographers with more than 10 years of experience, all in fetal cardiology. The entire process was supervised by another obstetrician with more than 25 years of experience.
- Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the three.
HeartAssist:
- 440 fetal heart images from hospitals.
- Data set acquired with SAMSUNG MEDISON's ultrasound systems.
- Mix of data from retrospective data collection and prospective data collection in clinical practice.
- All acquired images for training, tuning and validation were first classified into the correct views by three participating experts. Afterwards, corresponding anatomy areas were manually drawn for each of the image. The participating experts were composed of an obstetrician with more than 20 years of experience and two sonographers with more than 10 years of experience, all in fetal cardiology. The entire process was supervised by another obstetrician with more than 25 years of experience.
- Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the three.
BiometryAssist:
- 360 fetal biometry images collected at two hospitals.
- Data set acquired with SAMSUNG MEDISON's ultrasound systems.
- Mix of data from retrospective data collection and prospective data collection in clinical practice.
- All acquired images for training, tuning and validation were first classified into the correct views by three participating experts. Afterwards, corresponding anatomy areas were manually drawn for each of the image. The participating experts were composed of an obstetrician with more than 20 years of experience and two sonographers with more than 10 years of experience, all in fetal cardiology. The entire process was supervised by another obstetrician with more than 25 years of experience.
- Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the three.
Description of the test set, sample size, data source, and annotation protocol
Live ViewAssist:
- 3,900 fetal ultrasound images for each view for quality assessment test.
- Demographic distribution: Gender: Female, Age: Reproductive age, Ethnicity/Country: Americans and Koreans, BMI: Ranging from 17-45.4.
- Fetal ultrasound images divided depending on ISUOG and AIUM guidelines.
- Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the three.
EzVolume:
- A total of 200 test volumes (100 volumes are in 1st trimester and the remaining 100 volumes are in 2nd/3rd trimester).
- Demographic distribution: Gender: Female, Age: Reproductive age, Ethnicity/Country: Koreans, Americans, Italian, and British.
- Fetal ultrasound 3D volumes collected and divided into the 1st and 2nd/3rd trimesters of pregnancy.
- All acquired data were evaluated by independent clinical experts according to specific protocols.
- Data used for training, validation and evaluation purpose are completely separated from the ones during training process, and there is no overlap among the three.
UterineContour:
- Test set for segmentation: 450 sagittal uterus images.
- Test set for 3D coronal view adaptation: 30 sagittal images of uterus.
- Demographic distribution: Gender: Female, Age: Reproductive age, Ethnicity/Country: All Koreans.
- Sagittal uterus images divided into 4 phases: early proliferative, peri-ovulatory, secretory and post-menopause.
- Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the data.
ViewAssist:
- 1600 fetal ultrasound images and fetal biometry images.
- Demographic distribution: Gender: Female, Age: Reproductive age, Ethnicity/Country: Americans and Koreans, BMI: Ranging from 17-45.4.
- Fetal ultrasound images divided into 40 views, depending on the ISUOG and AIUM guidelines.
- Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the three.
HeartAssist:
- 440 fetal heart images.
- Demographic distribution: Gender: Female, Age: Reproductive age, Ethnicity/Country: Americans and Koreans, BMI: Ranging from 17-45.4.
- Fetal ultrasound images divided into 11 fetal heart views, depending on the AIUM guidelines.
- Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the three.
BiometryAssist:
- 360 fetal biometry images.
- Demographic distribution: Gender: Female, Age: Reproductive age, Ethnicity/Country: Americans and Koreans, BMI: Ranging from 17-45.4.
- Fetal ultrasound images divided into 8 views, depending on the ISUOG and AIUM guidelines.
- Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the three.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Live ViewAssist:
- Study Type: Quality assessment test (deep learning based quality assessment algorithm validation).
- Sample Size: 3,900 fetal ultrasound images for each view.
- Key Results: The average Cohen's kappa coefficient is 0.818 (threshold 0.7). The average speed is 30.06 FPS (threshold 20 FPS).
EzVolume:
- Study Type: Qualitative (acceptance rate) and quantitative (dices score) evaluations of segmentation performance.
- Sample Size: 200 test volumes (100 in 1st trimester, 100 in 2nd/3rd trimester).
- Key Results:
- Qualitative Results (Acceptance Rate %):
- 1st trimester: Fluid 98%, Fetus 96%, Umbilical-cord 80%, Placenta 86%, Uterus 89%. Acceptance Criteria: >70%.
- 2nd/3rd trimester: Fluid 92%, Head 94%, Body 84%, Limbs 83%, Umbilical-cord 82%, Placenta 85%, Uterus 87%. Acceptance Criteria: >70%.
- Quantitative Results (Mean DSC):
- 1st trimester (accepted cases): Fluid 0.96, Fetus 0.91, Umbilical-cord 0.68, Placenta 0.74, Uterus 0.93.
- 1st trimester (rejected cases): Fluid 0.17, Fetus 0.55, Umbilical-cord 0.37, Placenta 0.33, Uterus 0.32.
- 2nd/3rd trimester (accepted cases): Fluid 0.78, Head 0.94, Body 0.68, Umbilical-cord 0.67, Limbs 0.66, Placenta 0.75, Uterus 0.80.
- 2nd/3rd trimester (rejected cases): Fluid 0.25, Head 0.46, Body 0.29, Umbilical-cord 0.38, Limbs 0.39, Placenta 0.32, Uterus 0.30.
- Qualitative Results (Acceptance Rate %):
UterineContour:
- Study Type: Segmentation test and 3D coronal view adaptation test based on deep learning algorithm.
- Sample Size: Segmentation test - 450 sagittal uterus images; 3D coronal view adaptation test - 30 sagittal images of uterus.
- Key Results:
- Segmentation Test: The average dice-score of uterus is 96%, The average dice-score of endometrium is 92%.
- 3D Coronal View Adaptation Test: The proportion of appropriateness was evaluated as clinically diagnosable, with over 90% of all cases.
ViewAssist:
- Study Type: View recognition and anatomy annotation (segmentation) tests of a deep learning based algorithm.
- Sample Size: 1600 fetal ultrasound images and fetal biometry images.
- Key Results:
- View Recognition Test: The average recognition accuracy is 94.50% (threshold 89%).
- Anatomy Annotation (Segmentation) Test: The average dice-score is 0.892 (threshold 0.8).
HeartAssist:
- Study Type: View recognition, segmentation, and size measurement tests of a deep learning based algorithm for fetal hearts.
- Sample Size: 440 fetal heart images.
- Key Results:
- View Recognition Test: The average recognition accuracy is 95.00% (threshold 89%).
- Segmentation Test: The average dice-score is 0.876 (threshold 0.8).
- Size Measurement Test: The error rate of area measured value is 8% or less. The error rate of angle measured value is 4% or less. The error rate of circumference measured value is 11% or less. The error rate of diameter measured value is 11% or less.
BiometryAssist:
- Study Type: Segmentation and Size measurement tests of a deep learning based algorithm.
- Sample Size: 360 fetal biometry images.
- Key Results:
- Segmentation Test: The average dice-score is 0.928 (threshold 0.8).
- Size Measurement Test: The error rate of circumference measured value is 8% or less. The error rate of distance measured value is 4% or less. The error rate of NT, NB, IT measured value is 1mm or less.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Live ViewAssist:
- Cohen's kappa coefficient: 0.818 (threshold 0.7) for quality assessment.
- Speed: 30.06 FPS (threshold 20 FPS).
EzVolume:
- Acceptance Rate (qualitative evaluation).
- Mean DSC (DICE Similarity Coefficient) (quantitative evaluation).
UterineContour:
- Dice-score for segmentation.
- Proportion of appropriateness (clinical evaluation for 3D coronal view adaptation).
ViewAssist:
- Recognition accuracy for view recognition.
- Dice-score for anatomy annotation (segmentation).
HeartAssist:
- Recognition accuracy for view recognition.
- Dice-score for segmentation.
- Error rate for area, angle, circumference, and diameter measured values.
BiometryAssist:
- Dice-score for segmentation.
- Error rate for circumference, distance, NT, NB, IT measured values.
Predicate Device(s)
V8/XV8/XH8, V7/XV7/XH7, V6/XV6/XH6 Diagnostic Ultrasound System (K240631)
Reference Device(s)
HERA W10 Diagnostic Ultrasound System; HERA W9 Diagnostic Ultrasound System(K230084), SonoSync(K241302), Voluson Expert 22, Voluson Expert 20, Voluson Expert 18(K231965)
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.1550 Ultrasonic pulsed doppler imaging system.
(a)
Identification. An ultrasonic pulsed doppler imaging system is a device that combines the features of continuous wave doppler-effect technology with pulsed-echo effect technology and is intended to determine stationary body tissue characteristics, such as depth or location of tissue interfaces or dynamic tissue characteristics such as velocity of blood or tissue motion. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.
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October 11, 2024
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Samsung Medison Co., Ltd. Ju Jee Young Regulatory Affairs Specialist 3366 Hanseo-ro, Nam-myeon Hongcheon-gun, 25108 SOUTH KOREA
Re: K241971
Trade/Device Name: HERA Z20, R20, HERA Z30, R30 Diagnostic Ultrasound System Regulation Number: 21 CFR 892.1550 Regulation Name: Ultrasonic Pulsed Doppler Imaging System Regulatory Class: Class II Product Code: IYN, IYO, ITX, QIH Dated: July 5, 2024 Received: September 5, 2024
Dear Ju Jee Young:
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 (the 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 available 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.
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Ouality System (OS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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.
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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, YANNA S. KANG -S
Yanna Kang, Ph.D. Assistant Director Mammography and Ultrasound Team 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
3
Indications for Use
Submission Number (if known)
Device Name
HERA Z20, R20, HERA Z30, R30 Diagnostic Ultrasound System
Indications for Use (Describe)
The diagnostic ultrasound system and probes are designed to obtain ultrasound images and analyze body fluids.
The clinical applications include: Fetal/Obstetrics, Abdominal, Gynecology, Intra-operative, Pediatric, Small Organ, Neonatal Cephalic, Adult Cephalic, Trans-vaginal, Muscular-Skeletal (Conventional, Superficial), Urology, Cardiac Adult, Cardiac Pediatric, Thoracic, Trans-esophageal (Cardiac) and Peripheral vessel.
It is intended for use by, or by the order of, and under the supervision of, an appropriately trained healthcare professional who is qualified for direct use of medical devices. It can be used in hospitals, private practices, clinics and similar care environment for clinical diagnosis of patients.
Modes of Operation: 2D mode. Color Doppler mode. Pulsed Wave (PW) Doppler mode. Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TDI) mode, Tissue Doppler Wave (TDW) mode, Power Doppler (PD) mode, ElastoScan™ Mode, MV-Flow Mode, Multi Image mode (Dual Quad) Combined modes 3D/AD model
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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Traditional 510(k) K241971
510(k) Summary:
In accordance with 21 CFR 807.92, the following summary of information is provided:
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- Date Prepared - July 5, 2024
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Manufacturer 2. SAMSUNG MEDISON CO., LTD. 3366, Hanseo-ro, Nam-myeon, Hongcheon-gun, Gangwon-do, Republic of Korea
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Primary Contact Person 3. Ju, Jee-Young Regulatory Affairs Specialist Phone: +82.2.2194.0861 Fax: +82. 2.2194.0273 Email: jee.ju(@samsungmedison.com
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- Secondary Contact Person Ninad Gujar Vice President Phone: +1.978.564.8632 Fax: +1.978.564.8677 Email: ngujar@neurologica.com
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న. Proposed Device
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Common/Usual Name : Diagnostic Ultrasound System and Accessories -
- Proprietary Name : HERA Z20, R20, HERA Z30, R30 Diagnostic Ultrasound System
- Regulation Name : Ultrasonic pulsed doppler imaging system
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Regulatory Class : Class II -
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Product Code : IYN, IYO, ITX, QIH, LLZ -
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Regulation Number : 21 CFR 892.1550, 892.1560, 892.1570, 892.2050 -
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- Predicate Devices
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- Device Description
The HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system are a general purpose, mobile, software controlled, diagnostic ultrasound system. Their function is to acquire ultrasound data and to display the data as 2D mode, Color Doppler mode, Power Doppler (PD) mode, M mode, Pulsed Wave (PW) Doppler mode, Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TDI) mode, Tissue Doppler Wave (TDW) mode, ElastoScan Mode, Combined modes, MV-Flow mode, Multi-Image mode(Dual, Quad), 3D/4D mode.
5
The HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system also give the operator the ability to measure anatomical structures and offer analysis packages that provide information that is used to make a diagnosis by competent health care professionals. The HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system have a real time acoustic output display with two basic indices, a mechanical index and a thermal index, which are both automatically displayed.
- Indication for Use 8.
The diagnostic ultrasound system and transducers are designed to obtain ultrasound images and analyze body fluids.
The clinical applications include: Fetal/Obstetrics. Abdominal. Gynecology. Intra-operative. Pediatric, Small Organ, Neonatal Cephalic, Adult Cephalic, Trans-rectal, Trans-vaginal, Muscular-Skeletal (Conventional, Superficial), Urology, Cardiac Adult, Cardiac Pediatric, Thoracic, Trans-esophageal (Cardiac) and Peripheral vessel.
It is intended for use by, or by the order of, and under the supervision of, an appropriately trained healthcare professional who is qualified for direct use of medical devices. It can be used in hospitals, private practices, clinics and similar care environment for clinical diagnosis of patients.
Modes of Operation: 2D mode, Color Doppler mode, Power Doppler (PD) mode, M mode, Pulsed Wave (PW) Doppler mode, Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TDI) mode, Tissue Doppler Wave (TDW) mode, ElastoScan mode, MV-Flow mode, Combined modes, Multi-Image mode(Dual, Quad), 3D/4D mode.
- Technological Comparison to Predicate Devices 9.
The HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system employ the same fundamental scientific technology as its primary predicate device V8/XV8/XH8, V7/XV7/XH7, V6/XV6/XH6 Diagnostic Ultrasound System (K240631).
10. Determination of Substantial Equivalence
Comparison to Predicate: The HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system is substantially equivalent to the predicate devices with regard to intended use, imaging capabilities, technological characteristics and safety and effectiveness.
- The systems are all intended for diagnostic ultrasound imaging and fluid flow ● analysis.
- . The proposed HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system and the primary predicate V8/XV8/XH8, V7/XV7/XH7, V6/XV6/XH6(K240631) diagnostic ultrasound system have the same clinical intended use.
- The proposed HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system and . the primary predicate V8/XV8/XH8, V7/XV7/XH7, V6/XV6/XH6(K240631) diagnostic ultrasound system have the same imaging modes and modes of operation.
- . The proposed HERA Z20, R20, HERA Z30, R30 have included BiometryAssist, HeartAssist, ViewAssist previously cleared in the HERA W10/W9(K230084), based on AI technology, expanding the application as below,
a) ViewAssist, a cleared feature in the HERA W10/W9(K230084), has added
6
additional annotation parts (profile, bladder, spine) and has subdivided the view recognitions items as below.
. 4CV -> 4CV(Early diastole), 4CV(End diastole), 4CV(End systole)
- . LVOT → LVOT(Systole), LVOT(End diastole)
- . RVOT -> RVOT(Systole), RVOT (End diastole)
- . Kidney -> Kidney(Axial), Kidney(Sagittal), Kidney(Coronal)
- . MSP → MSP, MSP(Zoom)
b) BiometryAssist, a cleared feature in the HERA W10/W9(K230084), has expanded the measurement items (NB, IT)
c) HeartAssist, a cleared feature in the HERA W10/W9(K230084), has subdivided view recognition items as below.
. 4CV --> 4CV(Early diastole), 4CV(End diastole), 4CV(End systole) . LVOT → LVOT(Systole), LVOT(End diastole) . RVOT → RVOT(Systole), RVOT(End diastole)
- The proposed HERA Z20, R20, HERA Z30, R30 have included the EzCheck, . EzReport, EzFlow, EzStructure and Luminant for work-flow improvement, and PortraitVue for visualization purpose.
- . The proposed HERA Z20, R20, HERA Z30, R30 have included IOTA-SRrisk with the similar indications for use of IOTA ADNEX of V8/XV8/XH8, V7/XV7/XH7, V6/XV6/XH6, the primary predicate(K240631).
- The proposed HERA Z20, R20, HERA Z30, R30 have included EzVolume, an AI . feature with the similar indications for use of Fibroid Mapping of Voluson Expert 22/20/18(K231965).
- The proposed HERA Z20, R20, HERA Z30, R30 have included Live ViewAssist, an AI feature with the similar indications for use of SonoLyst Live of Voluson Expert 22/20/18(K231965).
- . The proposed HERA Z20, R20, HERA Z30, R30 have included UterineContour, an AI feature with the similar indications for use of BiometryAssist of V8/XV8/XH8, V7/XV7/XH7, V6/XV6/XH6, the primary predicate(K240631).
- The proposed HERA Z20, R20, HERA Z30, R30 have included two new . transducers CMV1-10, CMV1-10Z. Biocompatibility test has been conducted for the new transducers, and image performance test has been conducted for the new transducers.
- . The proposed HERA Z20, R20, HERA Z30, R30 have included a WiFi module.
- The proposed HERA Z20, R20, HERA Z30, R30 have included SonoSync, a cleared function in the predicate SonoSync(K241302), for diagnostic image viewing and review as the same indications for use of predicate.
- . The proposed HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system and
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primary predicate V8/XV8/XH8, V7/XV7/XH7, V6/XV6/XH6(K240631) diagnostic ultrasound system have the same capability in terms of performing measurements, capturing digital images, reviewing and reporting studies.
- The proposed HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system and . predicate V8/XV8/XH8, V7/XV7/XH7, V6/XV6/XH6(K240631) primary diagnostic ultrasound system have been designed in compliance with approved electrical and physical safety standards.
- The systems are manufactured with materials which have been evaluated and found . to be safe for the intended use of the device.
- . The systems have acoustic power levels which are below the applicable FDA limits.
-
- Summary of Non-Clinical Testing
The device has been evaluated for acoustic output, biocompatibility, software function, cleaning and disinfection effectiveness as well as thermal, electrical, electromagnetic and mechanical safety, and has been found to conform with applicable FDA guidances and medical device safety standards. The HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system and its applications comply with the following FDA-recognized standards.
| Reference No. | Title |
|---|---|
| IEC 60601-1 | AAMI ANSI ES60601-1:2005/(R)2012 and A1:2012, C1:2009/(R)2012 |
| and A2:2010/(R)2012 (Consolidated Text) Medical electrical equipment - | |
| Part 1: General requirements for basic safety and essential performance | |
| (IEC 60601-1:2005, MOD) | |
| IEC 60601-1-2 | IEC60601-1-2: 2020-09(4.1 Edition) , Medical electrical equipment - Part |
| 1-2: General requirements for basic safety and essential performance - | |
| EMC | |
| IEC 60601-2-37 | IEC 60601-2-37 Edition 2.0 2007, Medical electrical equipment - Part 2- |
| 37: Particular requirements for the basic safety and essential performance | |
| of ultrasonic medical diagnostic and monitoring equipment | |
| IEC 60601-4-2 | IEC TR 60601-4-2 Edition 1.0 2016-05, Medical electrical equipment - |
| Part 4-2: Guidance and interpretation - Electromagnetic immunity: | |
| performance of medical electrical equipment and medical electrical | |
| systems | |
| ISO10993-1 | AAMI / ANSI / ISO 10993-1:2009/(R)2013, Biological evaluation of |
| medical devices – Part 1: Evaluation and testing within a risk management | |
| process | |
| ISO14971 | ISO 14971:2019, Medical devices - Application of risk management to |
| medical devices | |
| NEMA UD 2-2004 | NEMA UD 2-2004 (R2009) Acoustic Output Measurement Standard for |
| Diagnostic Ultrasound Equipment Revision 3 |
8
[The Summary of Testing for Live ViewAssist]
- Summary test statistics or other test results including acceptance criteria or other information supporting the appropriateness of the characterized performance.
We tested on an area: Quality assessment ('acceptable' or 'not-acceptable').
□ Quality assessment test
் A deep learning based quality assessment algorithm was validated using 3,900 fetal ultrasound images for each view.
· The average Cohen's kappa coefficient is 0.818 (threshold 0.7)
- □ Time/duration test
- · The average speed is 30.06 FPS (threshold 20 FPS)
- Demographic distribution:
- □ Gender: Female
- □ Age: Reproductive age, specific age not collected
- □ Ethnicity/Country: Americans and Koreans
- | BMI: Ranging from 17-45.4, distributed across underweight, standard range or overweight categories
- Information about clinical subgroups and confounders present in the dataset:
- [ We divided the fetal ultrasound images, depending on the ISUOG and AIUM guidelines.
- Information about equipment and protocols used to collect images
- [ We acquired the data set with SAMSUNG MEDISON's ultrasound systems in order to secure diversity of the data set: Mix of data from retrospective data collection and prospective data collection in clinical practice.
- Information about how the reference standard was derived from the dataset (i.e. the "Truthing" process):
- | All acquired images for training, tuning and validation were first classified into the acceptable and not-acceptable views by three participating experts. Afterwards, corresponding anatomy areas were manually drawn for each of the image.
- □ The participating experts were composed of an obstetrician with more than 20 years of experience and two sonographers with more than 10 years of experience, all in fetal cardiology. The entire process was supervised by another obstetrician with more than 25 years of experience.
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- Description of how the independence of test data from training data was ensured:
- Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the three.
[The Summary of Testing for EzVolume]
- Summary test statistics or other test results including acceptance criteria or other information supporting the appropriateness of the characterized performance.
- The segmentation performance of Ez Volume tested by both qualitative (acceptance rate) and quantitative (dices score) evaluations.
- [ The qualitative evaluation was performed by clinical experts according to the following protocol:
-
- A test volume is first uploaded to HERA Z20 and HERA Z30 system.
-
- Then, EzVolume item button on the touch screen was pushed, here user can choose whether the input volume is 1st trimester or 2nd - 3rd trimester.
-
- The segmentation result is displayed with different colors for each structure. The clinical experts can also change the color, opacity, and visibility of each structure to evaluate the result.
-
- Repeated the process 1-3 for all test volumes and finally computed the acceptance rate for each label.
-
- | | Acceptance Criteria: The acceptance rate of each label (fetus, head, body, limbs, fluid, umbilical-cord, placenta, uterus segmentation) should be higher than 70%.
- | | The acceptance rate and the mean DSC for each label are computed from a total of 200 test volumes (100 volumes are in 1st trimester and the remaining 100 volumes are in 2nd/3rd trimester). The results show a positive correlation between the qualitative (acceptance rate) and the quantitative assessment (mean DSC) by independent clinical experts, indicating the adequacy of the test protocol.
Acceptance rate for each label - qualitative results:
| 1st trimester | |||||
|---|---|---|---|---|---|
| Label | Fluid | Fetus | Umbilical-cord | Placenta | Uterus |
| Acceptance Rate (%) | 98% | 96% | 80% | 86% | 89% |
| 2nd/3rd trimester | |||||||
|---|---|---|---|---|---|---|---|
| Label | Fluid | Head | Body | Limbs | Umbilical-cord | Placenta | Uterus |
| Acceptance rate (%) | 92% | 94% | 84% | 83% | 82% | 85% | 87% |
The mean DSC for the accepted and rejected cases - quantitative results:
| 1st trimester | |||||
|---|---|---|---|---|---|
| Label | Fluid | Fetus | Umbilical-cord | Placenta | Uterus |
| Mean DSC | |||||
| for accepted cases | 0.96 | 0.91 | 0.68 | 0.74 | 0.93 |
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| Mean DSC
| for rejected cases | 0.17 | 0.55 | 0.37 | 0.33 | 0.32 | ||
|---|---|---|---|---|---|---|---|
| 2nd/3rd trimester | |||||||
| Label | Fluid | Head | Body | Umbilical-cord | Limbs | Placenta | Uterus |
| Mean DSC | |||||||
| for accepted | |||||||
| cases | 0.78 | 0.94 | 0.68 | 0.67 | 0.66 | 0.75 | 0.80 |
| Mean DSC | |||||||
| for rejected cases | 0.25 | 0.46 | 0.29 | 0.38 | 0.39 | 0.32 | 0.30 |
■ Demographic distribution:
- Gender: Female
- Age: Reproductive age, specific age not collected
- Ethnicity/Country: Koreans, Americans, Italian, and British
- Information about clinical subgroups and confounders present in the dataset: Fetal ultrasound 3D volumes were collected and divided into the 1st and 2nd/3rd trimesters of pregnancy.
- I Information about equipment and protocols used to collect images We acquired the data set with SAMSUNG MEDISON's ultrasound and in order to secure diversity of the data set: Mix of data from retrospective data collection and prospective data collection in clinical practice.
- Information about how the reference standard was derived from the dataset
(i.e. the "Truthing" process):
All GTs (ground truths) of target region for training, validation and evaluation were drawn manually by four participating clinical experts. The participating experts were composed of an obstetrician with more than 20 years of experience and three examiners with more than 10 years of experience, all in fetal diagnosis. The entire process was supervised by another obstetrician with more than 25 years of experience.
-
Description of how the independence of test data from training data was ensured: Data used for training, validation and evaluation purpose are completely separated from the ones during training process, and there is no overlap among the three.
[ The Summary of Testing for UterineContour] -
Summary test statistics or other test results including acceptance criteria or other information supporting the appropriateness of the characterized performance.
-
We tested on two areas : image segmentation, 3D coronal view adaptation.
11
- □ Segmentation test
- A deep learning based segmentation algorithm was validated using 450 sagittal uterus images collected at three hospitals.
- ் The average dice-score of uterus is 96%
- · The average dice-score of endometrium is 92%
- 3D coronal view adaptation test
- · We acquired, in addition, 30 sagittal images of uterus collected at three hospitals.
- · As a result of the evaluation by clinical experts, the proportion of appropriateness was evaluated as clinically diagnosable, with over 90% of all cases.
- Demographic distribution:
- □ Gender : Female
- □ Age : Reproductive age, specific age not collected
- □ Ethnicity/Country : All Koreans
- Information about clinical subgroups and confounders present in the dataset:
- | | We divided the sagittal uterus images, depending on the shape of the endometrium, into 4 phases : early proliferative, peri-ovulatory, secretory and post-menopause.
- Information about equipment and protocols used to collect images
- We acquired the data set with SAMSUNG MEDISON's ultrasound systems and probes in order to secure diversity of the data set. Mix of data from retrospective data collection and prospective data collection in clinical practice
- Information about how the reference standard was derived from the dataset (i.e. the "truthing" process) :
- [] Segmentation of the ground truth was generated by three participating OB/GYN experts with more than 10 years' experience.
- | | Each expert delineated uterus and endometrium in sagittal and uterus in transverse images regarding all of the ground truth dataset respectively.
- | | The GT results of the 3 experts did not match was reviewed in three participating OB/GYN experts together and fixed the wrong part with consensus.
- Description of how independence of test data from training data was ensured:
- □ Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the data.
12
[The Summary of Testing for ViewAssist]
-
Summary test statistics or other test results including acceptance criteria or other information supporting the appropriateness of the characterized performance.
We tested on two areas: view recognition and anatomy annotation(segmentation). -
□ View recognition test
-
A deep learning based view recognition algorithm was validated using 1600 fetal ultrasound images and fetal biometry images collected at two hospitals.
-
· The average recognition accuracy is 94.50% (threshold 89%)
-
□ Anatomy annotation(segmentation) test
-
· We use same datasets of view recognition test.
-
· The average dice-score is 0.892 (threshold 0.8)
-
Demographic distribution:
- □ Gender: Female
- □ Age: Reproductive age, specific age not collected
- □ Ethnicity/Country: Americans and Koreans
- | BMI: Ranging from 17-45.4, distributed across underweight, standard range or overweight categories
-
Information about clinical subgroups and confounders present in the dataset:
- [ We divided the fetal ultrasound images, depending on the ISUOG and AIUM guidelines, into 40 views.
-
Information about equipment and protocols used to collect images
- □ We acquired the data set with SAMSUNG MEDISON's ultrasound systems in order to secure diversity of the data set: Mix of data from retrospective data collection and prospective data collection in clinical practice.
-
Information about how the reference standard was derived from the dataset
(i.e. the "Truthing" process):
- □ All acquired images for training, tuning and validation were first classified into the correct views by three participating experts. Afterwards, corresponding anatomy areas were manually drawn for each of the image.
- | | The participating experts were composed of an obstetrician with more than 20 years of experience and two sonographers with more than 10 years of experience, all in fetal cardiology. The entire process was supervised by another obstetrician with more than 25 years of experience.
13
- Description of how the independence of test data from training data was ensured:
- [ Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the three.
[The Summary of Testing for HeartAssist]
-
Summary test statistics or other test results including acceptance criteria or other information supporting the appropriateness of the characterized performance.
We tested fetal hearts in three areas: view recognition, segmentation and size measurement. -
□ View recognition test
-
் A deep learning based view recognition algorithm was validated using 440 fetal heart at the hospitals.
-
· The average recognition accuracy is 95.00% (threshold 89%)
-
□ Segmentation test
-
· We use same datasets of view recognition test.
-
் The average dice-score is 0.876 (threshold 0.8)
-
□ Size measurement test
-
· We use same datasets of segmentation test.
-
ು The error rate of area measured value is 8% or less
-
ං The error rate of angle measured value is 4% or less
-
ು The error rate of circumference measured value is 11% or less
-
· The error rate of diameter measured value is 11% or less
-
Demographic distribution:
- □ Gender: Female
- □ Age: Reproductive age, specific age not collected
- □ Ethnicity/Country: Americans and Koreans
- | BMI: Ranging from 17-45.4. distributed across underweight, standard range or overweight categories
-
Information about clinical subgroups and confounders present in the dataset:
- We divided the fetal ultrasound images, depending on the AIUM guidelines, into 11
14
Image /page/14/Picture/0 description: The image shows the word "SAMSUNG" in large, bold, blue letters. The font is sans-serif and the letters are evenly spaced. The word is centered and takes up most of the image space.
fetal heart views.
- I Information about equipment and protocols used to collect images
- □ We acquired the data set with SAMSUNG MEDISON's ultrasound systems in order to secure diversity of the data set: Mix of data from retrospective data collection and prospective data collection in clinical practice.
- Information about how the reference standard was derived from the dataset
(i.e. the "Truthing" process):
- [ All acquired images for training, tuning and validation were first classified into the correct views by three participating experts. Afterwards, corresponding anatomy areas were manually drawn for each of the image. The participating experts were composed of an obstetrician with more than 20 years of experience and two sonographers with more than 10 years of experience, all in fetal cardiology. The entire process was supervised by another obstetrician with more than 25 years of experience.
- Description of how the independence of test data from training data was ensured:
- [ Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the three.
[The Summary of Testing for BiometryAssist]
-
Summary test statistics or other test results including acceptance criteria or other information supporting the appropriateness of the characterized performance.
We tested on two areas: Segmentation and Size measurement. -
□ Segmentation test
-
· A deep learning based segmentation algorithm was validated using 360 fetal biometry images collected at two hospitals.
-
் The average dice-score is 0.928 (threshold 0.8)
-
□ Size measurement test
-
· We use same datasets of segmentation test.
-
· The error rate of circumference measured value is 8% or less.
-
· The error rate of distance measured value is 4% or less.
-
· The error rate of NT, NB, IT measured value is 1mm or less.
-
Demographic distribution:
15
- □ Gender: Female
- □ Age: Reproductive age, specific age not collected
- □ Ethnicity/Country: Americans and Koreans
- □ BMI: Ranging from 17-45.4, distributed across underweight, standard range or overweight categories
- Information about clinical subgroups and confounders present in the dataset:
- | We divided the fetal ultrasound images, depending on the ISUOG and AIUM guidelines, into 8 views.
- Information about equipment and protocols used to collect images
- □ We acquired the data set with SAMSUNG MEDISON's ultrasound systems in order to secure diversity of the data set: Mix of data from retrospective data collection and prospective data collection in clinical practice.
- Information about how the reference standard was derived from the dataset (i.e. the "Truthing" process):
- □ All acquired images for training, tuning and validation were first classified into the correct views by three participating experts. Afterwards, corresponding anatomy areas were manually drawn for each of the image.
- □ The participating experts were composed of an obstetrician with more than 20 years of experience and two sonographers with more than 10 years of experience, all in fetal cardiology. The entire process was supervised by another obstetrician with more than 25 years of experience.
- Description of how the independence of test data from training data was ensured:
- Data used for training, tuning and validation purpose are completely separated from the ones during training process and there is no overlap between the three.
12. Summary of Clinical Tests
The proposed device HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system did not require clinical studies to demonstrate substantial equivalence.
-
- Conclusion
Since the predicate devices and the subject device have a similar intended use and key technological features, the non-clinical data support the safety of the device and demonstrate that the HERA Z20, R20, HERA Z30, R30 diagnostic ultrasound system should perform as intended in the specified use conditions. Therefore, SAMSUNG MEDISON CO., LTD. considers the subject device to be as safe, as effective, and performance is substantially equivalent to the primary predicate device that is currently marketed for the same intended use.
- Conclusion
-
END of 510(k) Summary -