(204 days)
Not Found
Yes
The device description explicitly states that the software uses "AI/ML models".
No
The device is a software for image processing and measurement, intended to assist surgeons in planning procedures, not to directly treat a medical condition.
No
Explanation: The device is described as "image-processing software indicated to assist in making measurements" and "not for primary image interpretation." This indicates it provides tools for measurement and planning, not a diagnostic output or interpretation of disease status.
Yes
The device is explicitly described as "software as a medical device (SaMD) system" and its function is image processing and measurement assistance, which are software-based tasks. While it takes image input, there is no indication of accompanying hardware being part of the device itself.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- Device Function: Ortho AI is an image-processing software that analyzes radiographic images (X-rays) of the pelvis, knee, and spine. It assists in making measurements and planning for surgical procedures.
- Input: The input is medical imaging data, not biological samples from the patient.
- Purpose: The purpose is to aid in surgical planning and measurement based on anatomical structures visible in the images, not to diagnose a disease or condition from a biological sample.
The device falls under the category of medical imaging software or surgical planning software, which are distinct from IVDs.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
Ortho AI is an image-processing software indicated to assist in making measurements for a total hip arthroplasty, total knee arthroplasty, and lumbar spine fusion surgery.
It is intended to assist in the measurement of x-ray images by measuring lengths, angles and position of implants relative to the bone structures of interest provided, that the points of interest can be identified from radiology images.
The device allows for overlaying of digital annotations on radiological images and includes tools for performing measurements using the images and digital annotations. The software is not for primary image interpretation. The software is not for use on mobile phones.
Product codes
QIH
Device Description
Ortho AI is a software as a medical device (SaMD) system that provides preoperative planning data for hip replacement surgery, knee replacement surgery, and lumbar spinal fusion surgery using AI/ML models that are semi-automated and interpretable. The software guides the user through a predetermined workflow that begins with the use of preoperative radiographic images as input to the software. As part of this initial preoperative workflow, the software places digital annotations on these preoperative images, which can be modified by the user (semi-automated). The software additionally includes functionality to store user, patient, and case information.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
preoperative radiographic images of the pelvis, knee, or spine. Input mages may be one of a large number of standard image formats that are supported (.jpg, .bmp, etc.), a DICOM image. The image can also be uploaded intraoperatively from a fluoroscopic c-arm via a direct cable connection.
Anatomical Site
Hip, Knee, Lumbar Spine
Indicated Patient Age Range
Adult patients >=22 years old
Intended User / Care Setting
Intended user population: orthopaedic surgeons who perform hip and knee replacement, and orthopaedic/neurosurgeons who perform lumbar spine surgery.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Truthing process: 3 fellowship-trained ABOS board-certified orthopaedic surgeons with each greater than 10 years of experience, followed a 2+1 truthing process where two blinded orthopaedic surgeons reviewed each segmented image in the testing set and applied modifications. After the reviews from each blinded surgeon, a final senior-level surgeon adjudicator reviewed the modifications and added further modifications to the segmentations, if necessary.
Hip model:
- 1,367 images from 1,367 patients
- Average age: 62
- Female: 52%
- Ethnicity: 78% white, 18% Black/African American, 1% Other (non-white), 3% Asian
Hip-spine model:
- 4,836 images from 4,836 patients
- Average age: 62
- Female: 61%
- Ethnicity: 78% white, 18% Black/African American, 1% Other (non-white), 3% Asian
Knee model:
- 4,536 images from 4,536 patients
- Average age: 61
- Female: 52%
- Ethnicity: 78% white, 18% Black/African American, 1% Other (non-white), 3% Asian
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Software Validation and Verification was performed.
Acceptance Criteria and Sample size
The overall aim was to achieve a Dice coefficient greater than 0.85 (Dice > 0.85). A test sample size of >= 150 samples and observing an effect size of >= 0.01370, we can be confident achieving a statistical power of >= 80%.
Acceptance criteria for a mean Dice coefficient at a minimum of 0.85 for all algorithms and all connected domains.
Model summary:
Data independence: We ensured full data independence by adopting a patient-level partitioning process, where each patient contributed only a single x-ray image to the dataset, eliminating any risk of intersample dependencies or data leakage between the training, validation, and testing phases.
Standalone algorithm testing
Hip model:
- LLD measurements within +/- 1.96mm of human measurement
- Offset (global) within +/- 0.88mm of human measurement
- SFP angle within +/- 1.05mm of human measurement
- Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 0.85.
- Subgroup analysis by x-ray machine also shows that all connected domains, across all x-ray machine types, to be above our acceptance criteria of 0.85.
Hip-spine model:
- SS, SPT, APPt, PI, LL, PI-LL all within 2 degrees of human measurement
- No statistical difference between human vs. machine learning measurements
- Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 0.85.
- Subgroup analysis by x-ray machine also shows that all connected domains, across all x-ray machine types, to be above our acceptance criteria of 0.85.
Knee model:
- LDFA , mPTA, aHKA, aJLOA all within 2 degrees of human measurement
- No statistical difference between human vs. machine learning measurements
- Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 0.85.
- Subgroup analysis by x-ray machine also shows that all connected domains, across all x-ray machine types, to be above our acceptance criteria of 0.85.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Dice coefficient, measurement differences (mm, degrees) between algorithm and human measurements.
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
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
0
Image /page/0/Picture/0 description: The image contains the logos of the Department of Health and Human Services and the U.S. Food & Drug Administration (FDA). The Department of Health and Human Services logo is on the left, and the FDA logo is 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.
Ortho AI LLC Jonathan Vigdorchik Orthopaedic Surgeon 155 East 76th Street Apt 2H New York, NY 10021
January 2, 2025
Re: K241696
Trade/Device Name: Ortho AI Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: November 26, 2024 Received: November 27, 2024
Dear Jonathan Vigdorchik:
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.
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"
1
(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 Quality System (QS) 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 (OS) 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.
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-regulatory
2
assistance/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,
Jessica Lamb
Jessica Lamb, Ph.D Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products 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
Ortho Al
Indications for Use (Describe)
Ortho AI is an image-processing software indicated to assist in making measurements for a total hip arthroplasty, total knee arthroplasty, and lumbar spine fusion surgery.
It is intended to assist in the measurement of x-ray images by measuring lengths, angles and position of implants relative to the bone structures of interest provided, that the points of interest can be identified from radiology images.
The device allows for overlaying of digital annotations on radiological images and includes tools for performing measurements using the images and digital annotations. The software is not for primary image interpretation. The software is not for use on mobile phones.
Intended patient population: Adult patients >=22 years old, with appropriate imaging, undergoing primary hip replacement, primary knee replacement, and lumbar spine surgery.
Intended user population: orthopaedic surgeons who perform hip and knee replacement, and orthopaedic/neurosurgeons who perform lumbar spine surgery.
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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4
510(k) Summary
| Device Trade Name: | Ortho AI |
|---|---|
| Manufacturer: | Ortho AI |
| 155 East 76th Street Apt 2H | |
| New York, NY 10021 | |
| Phone: 314-662-3222 | |
| Contact: | Jonathan Vigdorchik |
| Ortho AI LLC | |
| 155 East 76th Street | |
| Apt 2H | |
| New York, NY 10021 | |
| Phone: 314-662-3222 | |
| jonnyvmd@gmail.com | |
| Prepared By: | MCRA, LLC |
| 803 7th St NW | |
| Washington, DC 20001 | |
| Office: 202.552.5800 | |
| Date Prepared: | November 25, 2024 |
| Device Trade Name: | Ortho AI |
| Classification: | 892.2050 Medical image management and processing system |
| Class: | Class II |
| Product Code: | QIH |
| Predicate Device: | KOALA - K192109 |
| Classification: | 892.2050 Medical image management and processing system |
| Class: | Class II |
| Product Code: | QIH |
Indications for Use:
Ortho AI is an image-processing software indicated to assist in making measurements for a total hip arthroplasty, total knee arthroplasty, and lumbar spine fusion surgery.
It is intended to assist in the measurement of x-ray images by measuring lengths, angles and position of implants relative to the bone structures of interest, provided that the points of interest can be identified from radiology images.
The device allows for overlaying of digital annotations on radiological images and includes tools for performing measurements using the images and digital annotations. The software is not for primary image interpretation. The software is not for use on mobile phones.
5
Intended patient population: Adult patients >= 22 years old, with appropriate imaging, undergoing primary hip replacement, primary knee replacement, and lumbar spine surgery.
Intended user population: orthopaedic surgeons who perform hip and knee replacement, and orthopaedic/neurosurgeons who perform lumbar spine surgery.
Device Description:
Ortho AI is a software as a medical device (SaMD) system that provides preoperative planning data for hip replacement surgery, knee replacement surgery, and lumbar spinal fusion surgery using AI/ML models that are semi-automated and interpretable. The software guides the user through a predetermined workflow that begins with the use of preoperative radiographic images as input to the software. As part of this initial preoperative workflow, the software places digital annotations on these preoperative images, which can be modified by the user (semi-automated). The software additionally includes functionality to store user, patient, and case information.
Software/firmware input
As a non-invasive software as a medical device (SaMD) system, Ortho AI requires, as input, preoperative radiographic images of the pelvis, knee, or spine. Input mages may be one of a large number of standard image formats that are supported (.jpg, .bmp, etc.), a DICOM image. The image can also be uploaded intraoperatively from a fluoroscopic c-arm via a direct cable connection. Images must be uploaded by a healthcare professional.
Software/firmware output
The software output will be an HTML page that displays the processed image in JPEG format. The output displays the measurements of angles and length of anatomical structures that are essential for preoperative planning for the anatomical site displayed in the image. The output can only be viewed by a healthcare professional.
Software functions
Ortho AI is a software-only device intended to be used by healthcare professionals for the preoperative and intraoperative planning of total hip replacement surgery, partial/total knee replacement surgery, or lumbar spine surgery. The software semi-automates templating/planning for surgery. In the standard of care pre-operative planning procedure, a healthcare provider templates manually or digitally on a radiograph by drawing lines, making angular measurements, and sizing implants using acetate or digital templates. The Ortho AI software will semi-automate the above listed tasks by taking a radiological image as input and delivering key measurements of the surgeon's interest through an AI algorithm. These landmarks are then editable by the user.
| Feature | Subject Device | Predicate Device | Comparison |
|---|---|---|---|
| 510(k) Number | K241696 | K192109 | N/A |
| Name | Ortho AI | IB Lab's KOALA | |
| Software | N/A | ||
| Classification | |||
| Name and | |||
| Product Code | System, Image | ||
| Processing, | |||
| Radiological (QIH) | System, Image | ||
| Processing, | |||
| Radiological (LLZ) | Same |
Substantial Equivalence:
6
| Runs on Server | Yes | Yes | Same |
|---|---|---|---|
| Image input | JPEG, PNG, BMP, | ||
| DICOM | DICOM compliant | ||
| images collected in other | |||
| devices in either digitally | |||
| computed (CR) or | |||
| directly digital (DX) | |||
| formats. | Similar. The subject | ||
| device is validated to | |||
| additionally analyze other | |||
| standard image formats | |||
| and does not raise new | |||
| questions about safety or | |||
| effectiveness. | |||
| Imaging | |||
| processing | Landmark detection, user | ||
| can edit landmark | Knee detection; | ||
| Landmark detection; | |||
| Joint space detection | Same | ||
| Anatomical | |||
| Area | Knee, Hip, Lumbar Spine | Leg | Similar. The subject |
| device is intended to be | |||
| used on the knee, hip, and | |||
| lumbar spine while the | |||
| predicate device is only | |||
| for the leg. This | |||
| difference in specific | |||
| anatomic locations does | |||
| not raise new types of | |||
| questions for safety or | |||
| effectiveness. | |||
| Measurement | Length and angle | Length and angle | Same |
| Way of | |||
| Measurement | Semi-automatic (AI | ||
| driven) | Automatic (AI driven) | Similar. The subject | |
| device requires the user | |||
| to review and edit the | |||
| device output as | |||
| necessary while the | |||
| predicate device does not | |||
| as a fully automatic | |||
| device. | |||
| Intended User | Trained Professional | Trained Professional | Same |
| Indications for | |||
| Use | Ortho AI is an image- | ||
| processing software | |||
| indicated to assist in | |||
| making measurements for | |||
| a total hip arthroplasty, | |||
| total knee arthroplasty, | |||
| and lumbar spine fusion | |||
| surgery. |
It is intended to assist in
the measurement of x-ray
images by measuring
lengths, angles and
position of implants
relative to the bone
structures of interest,
provided that the points
of interest can be | IB Lab KOALA is a
radiological fully-
automated image
processing software
device of either computed
(CR) or directly digital
(DX) images intended to
aid medical professionals
in the measurement of
minimum joint space
width; the assessment of
the presence or absence
of sclerosis, joint space
narrowing, and
osteophytes based
OARSI criteria for these
parameters; and the
presence or absence of | The subject device
performs measurements
of lengths and angles on
hip, knee, and lumbar
spine images. The
predicate device performs
length and angle
measurements on leg
images. This difference in
specific anatomic
locations does not raise
new questions for safety
or effectiveness and
therefore does not induce
changes in the intended
use |
7
| identified from radiology | radiographic knee OA |
|---|---|
| images. The device allows for overlaying of digital annotations on radiological images and includes tools for performing measurements using the images and digital annotations. The software is not for primary image interpretation. The software is not for use on mobile phones. Intended patient population: Adult patients >=22 years of age, with appropriate imaging, undergoing primary hip replacement, primary knee replacement, and lumbar spine surgery. Intended user population: orthopaedic surgeons who perform hip and knee replacement, and orthopaedic/neurosurgeon s who perform lumbar spine surgery. | based on Kellgren & Lawrence Grading of standing, fixed-flexion radiographs of the knee. It should not be used in- lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. The system is to be used by trained professionals including, but not limited to, radiologists, orthopedics, physicians, and medical technicians. |
Performance Testing Summary:
Software Validation and Verification was performed.
Acceptance Criteria and Sample size
The overall aim was to achieve a Dice coefficient greater than 0.85 (Dice > 0.85). A test sample size of ≥ 150 samples and observing an effect size of ≥ 0.01370, we can be confident achieving a statistical power of ≥ 80%.
Acceptance criteria for a mean Dice coefficient at a minimum of 0.85 for all algorithms and all connected domains.
Model summary:
Data independence: We ensured full data independence by adopting a patient-level partitioning process, where each patient contributed only a single x-ray image to the dataset, eliminating any risk of intersample dependencies or data leakage between the training, validation, and testing phases.
8
Truthing process: 3 fellowship-trained ABOS board-certified orthopaedic surgeons with each greater than 10 years of experience, followed a 2+1 truthing process where two blinded orthopaedic surgeons reviewed each segmented image in the testing set and applied modifications. After the reviews from each blinded surgeon, a final senior-level surgeon adjudicator reviewed the modifications and added further modifications to the segmentations, if necessary.
Hip model:
- 1,367 images from 1,367 patients -
- Average age: 62 -
- Female: 52% -
- Ethnicity: 78% white, 18% Black/African American, 1% Other (non-white), 3% Asian -
Hip-spine model:
- 4,836 images from 4,836 patients -
- Average age: 62 -
- Female: 61% -
- -Ethnicity: 78% white, 18% Black/African American, 1% Other (non-white), 3% Asian
Knee model:
- -4,536 images from 4,536 patients
- Average age: 61 -
- -Female: 52%
- Ethnicity: 78% white, 18% Black/African American, 1% Other (non-white), 3% Asian -
Standalone algorithm testing
Hip model:
- LLD measurements within +/- 1.96mm of human measurement -
- -Offset (global) within +/- 0.88mm of human measurement
- SFP angle within +/- 1.05mm of human measurement -
- Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 0.85.
- Subgroup analysis by x-ray machine also shows that all connected domains, across all x-ray machine types, to be above our acceptance criteria of 0.85.
Hip-spine model:
- SS, SPT, APPt, PI, LL, PI-LL all within 2 degrees of human measurement -
- No statistical difference between human vs. machine learning measurements -
- Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 0.85.
- Subgroup analysis by x-ray machine also shows that all connected domains, across all x-ray machine types, to be above our acceptance criteria of 0.85.
Knee model:
- LDFA , mPTA, aHKA, aJLOA all within 2 degrees of human measurement -
- -No statistical difference between human vs. machine learning measurements
9
- Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 0.85.
- Subgroup analysis by x-ray machine also shows that all connected domains, across all x-ray machine types, to be above our acceptance criteria of 0.85.
Conclusion
Ortho AI demonstrates substantial equivalence to the predicate device. The subject device has the same intended use and principles of operation; furthermore, it has similar indications and technological characteristics as its predicate device. The minor differences between subject and predicate device in indications do not alter the intended use of the subject device and do not raise new or different questions regarding its safety and effectiveness when used as labeled. Performance data demonstrate that the device performs as intended. Verification and validation testing, including the standalone software performance test, supports the safety of the device and demonstrates that Ortho AI performs as intended. Therefore, Ortho AI demonstrates substantial equivalence to the predicate device.