← Product Code [QIH](/productcode/QIH) · K253172

# MediAI-OA (K253172)

_Crescom Co., Ltd. · QIH · Jun 15, 2026 · Radiology · SESE_

**Canonical URL:** https://fda.innolitics.com/device/K253172

## Device Facts

- **Applicant:** Crescom Co., Ltd.
- **Product Code:** [QIH](/productcode/QIH.md)
- **Decision Date:** Jun 15, 2026
- **Decision:** SESE
- **Submission Type:** Traditional
- **Regulation:** 21 CFR 892.2050
- **Device Class:** Class 2
- **Review Panel:** Radiology
- **Attributes:** AI/ML, Software as a Medical Device

## Indications for Use

MediAI-OA 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 assessment of the presence or absence of sclerosis, joint space narrowing, and osteophytes based on OARSI criteria for these parameters; and, the presence or absence of radiographic knee OA 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, orthopedists, physicians and medical technicians.

## Device Story

MediAI-OA is an orthopedic image, computer-aided detection/diagnosis software; analyzes DICOM-compliant knee X-ray images (CR/DX); uses deep learning algorithms to perform knee detection, landmark detection, and joint space detection; outputs include KL grade (0-4), JSN status, osteophyte status, and sclerosis status; used in healthcare facilities by trained professionals (radiologists, orthopedists, physicians, technicians); provides summary analysis via screen, web report, and markup images; assists clinicians in diagnosing degenerative arthritis; does not replace full patient evaluation.

## Clinical Evidence

Independent clinical performance test using 1,173 subjects (11,383 knees) from the Osteoarthritis Initiative (OAI) dataset, partitioned by site to prevent data leakage. Primary endpoints: sensitivity and specificity for KL grade, JSN, osteophytes, and sclerosis. Results: KL grade ≥2 sensitivity 0.876, specificity 0.865. JSN (≥OARSI grade 1) sensitivity 0.880, specificity 0.864. Osteophyte (≥OARSI grade 1) sensitivity 0.897, specificity 0.823. Sclerosis sensitivity 0.88, specificity 0.90. Performance metrics demonstrate substantial equivalence to manual annotations by trained readers.

## Technological Characteristics

Automated radiological image processing software; deep learning algorithm; runs on server; inputs DICOM (CR/DX), JPEG, JPG, PNG; outputs static markup images and textual reports. Complies with IEC 62304 (software lifecycle), IEC 62366-1 (usability), ISO 14971 (risk management), and cybersecurity standards (SW96:2023, 81001-5-1, TIR57).

## Regulatory 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.

## Special Controls

*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).

## Predicate Devices

- DeepXray ([K223621](/device/K223621.md))

## Submission Summary (Full Text)

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>
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**FDA** **U.S. FOOD & DRUG**
ADMINISTRATION

June 15, 2026

CRESCOM Co., Ltd.
Rosa Han
Consultant
Room 801, 8, Seongnam-daero 331beon-gil, Bundang-gu
Seongnam-si, Gyeonggi-do 13558
Republic Of Korea

Re: K253172

Trade/Device Name: MediAI-OA

Regulation Number: 21 CFR 892.2050

Regulation Name: Medical Image Management And Processing System

Regulatory Class: Class II

Product Code: QIH

Dated: May 17, 2026

Received: May 18, 2026

Dear Rosa Han:

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.

U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov

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K253172 – Rosa Han

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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 Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13485 clause 8.3 (Nonconforming product), ISO 13485 clause 8.5.2 (Corrective action), and ISO 13485 clause 8.5.3 (Preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and ISO 13485 clause 7.5) and document changes and approvals in the Medical Device File (ISO 13485 clause 4.2.3).

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 (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-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (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-devices/device-advice-comprehensive-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-devices/medical-device-safety/medical-device-reporting-mdr-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/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the

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K253172 – Rosa Han

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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-devices/device-advice-comprehensive-regulatory-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, 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

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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration

Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.

# Indications for Use

510(k) Number (if known)
K253172

Device Name
MediAI-OA

Indications for Use (Describe)

MediAI-OA 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 assessment of the presence or absence of sclerosis, joint space narrowing, and osteophytes based on OARSI criteria for these parameters; and, the presence or absence of radiographic knee OA 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, orthopedists, physicians and medical technicians.

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)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

*DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.*

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PSC Publishing Services (301) 443-6740 EF

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K253172

CRESCOM

## 1. 510(k) Summary

This summary of 510(k) information is being submitted in accordance with requirements of 21 CFR Part 807.92.

- • **Date** May 08, 2026
- • **Applicant / Submitter** CRESCOM Co., Ltd.  
  Room 801, 8, Seongnam-daero 331beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13558, Republic of Korea  
  Phone +82-70-4820-3843
- • **Device Information**  
  Device(Trade) Name: MediAI-OA  
  Classification Name: Automated Radiological Image Processing Software  
  Regulation Number: 21 CFR 892.2050  
  Regulation Name: Medical image management and processing system  
  Regulatory Class: Class II  
  Product Code: QIH
- • **Predicate Device**  
  Manufacturer: Alpha Intelligence Manifolds, Inc.  
  Device(Trade) Name: DeepXray  
  510(k) Number: K223621  
  Regulation Name: Medical image management and processing system  
  Regulatory Number: 21 CFR 892.2050  
  Regulatory Class: Class II  
  Product Code: QIH
- • **Identification of a Legally Marketed Predicate Device;**  
  MediAI-OA is substantially equivalent to the DeepXray marketed by Alpha Intelligence Manifolds, Inc., 510(k) Premarket Notification Number K223621, FDA Product Code QIH.
- • **General Description**  
  MediAI-OA is “Orthopedic image, computer aided detection/ diagnosis software” that automatically analyzes X-ray images of patients suspected of having osteoarthritis. It assists healthcare providers in making accurate and consistent diagnoses of knee osteoarthritis by providing information such as KL (Kellgren-Lawrence) grade, osteophyte grade, sclerosis, and JSN Grade.
- • **Indication for Use**

MediAI-OA 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 assessment of the presence or absence of sclerosis, joint space narrowing, and osteophytes based on OARSI criteria for these parameters; and, the presence or absence of radiographic knee OA 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, orthopedists, physicians and medical technicians.

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# - • **Comparison of Technological Characteristics**

The MediAI-OA and the predicate device DeepXray (K223621) are orthopedic imaging detection and diagnostic support software that assists physicians in diagnosing degenerative arthritis by analyzing knee X-ray images. It displays the KL grade (Kellgren-Lawrence Grade) as well as the presence or absence of JSN, osteophytes, and sclerosis.

Both devices employ deep learning algorithms to analyze input images and the severity of arthritis.

They operate in healthcare facilities and hospitals, are intended for trained professionals including, but not limited to, radiologists, orthopedists, physicians and medical technicians, and are classified under 21 CFR 892.2050, Product Code QIH, Class II.

|   | Subject Device | Predicate Device (K223621)  |
| --- | --- | --- |
|  **Device Name** | MediAI-OA | DeepXray  |
|  **Classification Name** | Automated Radiological Image Processing Software | Automated Radiological Image Processing Software  |
|  **Product Code** | QIH | QIH  |
|  **Intended Use /Indication for Use** | MediAI-OA 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 assessment of the presence or absence of sclerosis, joint space narrowing, and osteophytes based on OARSI criteria for these parameters; and, the presence or absence of radiographic knee OA 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, orthopedists, physicians and medical technicians. | DeepXray 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 on OARSI criteria for these parameters; and, the presence or absence of radiographic knee OA 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, orthopedists, physicians and medical technicians.  |
|  **Anatomical Area** | Joint (knee) | Joint (knee)  |
|  **Image Input** | DICOM compliant images in either digitally computed (CR) or directly digital (DX) formats, jpeg, jpg, png. | DICOM compliant images in either digitally computed (CR) or directly digital (DX) formats  |
|  **Image Processing** | Knee detection; Landmark detection; Joint space detection | Knee detection; Landmark detection; Joint space detection  |
|  **Human Intervention for interpretation** | Required | Required  |
|  **Intended User** | Trained professionals | Trained professionals  |
|  **Output Format** | Summary analysis results through the screen, Web report, Markup images and textual report as static images | Web report with quality warning, markup images and editing interface  |
|  **Output Information** | - Knee OA status: KL grade 0/1, 2, 3 or 4 - JSN status: Absent/Present - Osteophyte status: Absent/Present - Sclerosis status: Absent/Present | - Knee OA status: KL grade ≥2 or ≤1 - JSN status: Absent/Present - Osteophyte status: Absent/Present - Sclerosis status: Absent/Present  |
|  **Runs on Server** | Yes | Yes  |

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# - • **Brief Summary of Non-Clinical Tests and Results**

The MediAI-OA complies with the following international and FDA-recognized consensus standards list in the below table.

|  Recognition No. | Standard No. | Title of Standard | Remark  |
| --- | --- | --- | --- |
|  13-79 | IEC 62304 Edition 1.1 2015-06 CONSOLIDATED VERSION | Medical device software - Software life cycle processes |   |
|  5-129 | IEC 62366-1 Edition 1.1 2020-06 CONSOLIDATED VERSION | Medical devices - Part 1: Application of usability engineering to med |   |
|  5-125 | ISO 14971 Third Edition 2019-12 | Medical devices - Application of risk management to medical device |   |
|  13-131 | SW96:2023 | Standard for medical device security - Security risk management for device manufacturers |   |
|  13-122 | 81001-5-1 Edition 1.0 2021-12 | Health software and health IT systems safety, effectiveness and security - Part 5-1: Security - Activities in the product life cycle |   |
|  13-83 | TIR57:2016 | Principles for medical device security - Risk management. |   |

And MediAI-OA comply with the FDA guidance documents listed in the below table.

|  Title of Guidance Document | Issue Date  |
| --- | --- |
|  Guidance for Industry and Food and Drug Administration Staff: The 510(k) Program: Evaluating Substantial Equivalence in Premarket Notifications [510(k)] | July 28, 2014  |
|  Content of Premarket Submissions for Device Software Functions, Guidance for Industry and Food and Drug Administration Staff | June 14, 2023  |
|  Off-The-Shelf Software Use in Medical Devices | August 11, 2023  |
|  Cybersecurity in Medical Devices Quality System Considerations and Content of Premarket Submissions | February 3, 2026  |

The risk analysis was completed and risk controls were implemented to mitigate identified hazards.

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# • Brief Summary of Performance Tests and Results

CRESCOM conducted an independent clinical performance test of MediAI-OA. The test dataset was configured as follows. Site-based Data Partitioning To prevent data leakage and ensure the clinical validity of the algorithm, the study datasets were strictly partitioned by Clinical Site IDs rather than by individual patient randomization. The OAI protocol identifies multiple independent recruitment centers distributed across different geographic locations. Each site maintained its own participant pool and initial data files. The performance test was conducted using data from sites that were completely separate from those used for training and validation. The X-ray equipment manufacturers used for the images included Agfa, Fujifilm, GE and etc. (None of the cases used in this study were utilized for training or validation of the MediAI-OA model.)

|  Characteristics | Category |   | Training & Validation Set |   | Independent Test Set  |   |
| --- | --- | --- | --- | --- | --- | --- |
|  Gender | Female |   | 2,156 (59.5%) |   | 648 (55.2%)  |   |
|   |  Male |   | 1,467 (40.5%) |   | 525 (44.8%)  |   |
|  Age (Years) | Age ≥ 60 |   | 1,904 (52.6%) |   | 681 (58.0%)  |   |
|   |  Age < 60 |   | 1,719 (47.4%) |   | 492 (42.0%)  |   |
|  Race | Other Non-white |   | 70 (2.0%) |   | 12 (1.0%)  |   |
|   |  White |   | 2,808 (77.5%) |   | 982 (83.8%)  |   |
|   |  Black or African American |   | 709 (19.6%) |   | 165 (14.0%)  |   |
|   |  Asian |   | 31 (0.8%) |   | 14 (1.2%)  |   |
|   |  Unknown or not reported |   | 5 (0.1%) |   | 0 (0.0%)  |   |
|  Total Subjects |   |   | 3,623 |   | 1,173  |   |
|  KL | KL 0/1 | KL < 2 | 22,253 |   | 6,598  |   |
|   |  KL 2 | KL ≥ 2 | 10,212 | 18,912 | 2,799 | 4,785  |
|   |  KL 3 |   | 5,813 |   | 1,574  |   |
|   |  KL 4 |   | 2,887 |   | 412  |   |
|  Total Knees |   |   | 41,165 |   | 11,383  |   |
|  JSN | OARSI Grade 0 |   | 53,441 |   | 17,903  |   |
|   |  OARSI Grade ≥1 |   | 13,803 |   | 4,861  |   |
|  Total JSN |   |   | 67,244 |   | 22,764  |   |
|  Osteophyte | OARSI Grade 0 |   | 20,138 |   | 6,050  |   |
|   |  OARSI Grade ≥1 |   | 20,325 |   | 5,056  |   |
|  Total Osteophyte |   |   | 40,493 |   | 11,106  |   |
|  Sclerosis | Absent |   | 28,357 |   | 8,164  |   |
|   |  Present |   | 4,326 |   | 2,822  |   |
|  Total Sclerosis |   |   | 32,683 |   | 10,986  |   |

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|  Characteristics | Category | Training & Validation Set | Independent Test Set  |
| --- | --- | --- | --- |
|  Modality | CR | 10,219 | 5,444  |
|   |  DX | 2,935 | 246  |
|   |  RG | 3,657 | 0  |
|   |  Unknown | 0 | 1  |
|  X-ray Manufacturer | Agfa | 3,826 | 4,600  |
|   |  Fujifilm | 2,577 | 870  |
|   |  GE | 2,946 | 220  |
|   |  Konica-Minolta | 2,008 | 0  |
|   |  Pilips | 35 | 0  |
|   |  Siemens | 302 | 0  |
|   |  Swissray | 3,468 | 0  |
|   |  Others (Not reported) | 1,649 | 1  |
|  Total DICOM |   | 16,811 | 5,691  |
|  Visiting Timepoint (Follow-up Period) | Baseline | 11,492 | 1,114  |
|   |  12 months | 3,097 | 1,049  |
|   |  24 months | 2,917 | 962  |
|   |  36 months | 2,763 | 918  |
|   |  48 months | 2,610 | 872  |
|   |  72 months | 1,031 | 380  |
|   |  96 months | 1,061 | 396  |

All annotations used as the reference standard were manually performed by qualified annotators who were rigorously trained in accordance with the OAI (Osteoarthritis Initiative) Standardized Reading Protocol. These annotations were used exclusively as ground truth during the model development and internal validation phases.

During the performance evaluation (inference) stage, the algorithm automatically generates all required diagnostic outputs, such as KL grade and sclerosis detection, without any manual intervention or prior knowledge of the reference labels. Furthermore, to ensure the objectivity of the performance evaluation, the datasets for training, validation, and testing were strictly separated, thereby eliminating any risk of data leakage.

The clinical performance test was conducted by comparing the software's osteoarthritis assessment results with the reference standard derived from the OAI dataset, using sensitivity and specificity as evaluation metrics. As a result, MediAI-OA was confirmed to meet all of the predefined performance criteria described below.

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CRESCOM

- For KL grade classification, both sensitivity and specificity across all grades met performance targets comparable to those reported in previously conducted studies. In particular, for the detection of KL grade ≥ 2, the sensitivity was 0.876 (95% CI: 0.866–0.885) and the specificity was 0.865 (95% CI: 0.856–0.873). The lower bounds of the confidence intervals exceeded the predefined acceptance criteria, indicating strong discriminatory performance between normal and osteoarthritis cases.

Additionally, the sensitivity by KL grade was 0.865 (95% CI: 0.856–0.873) for grades 0 & 1, 0.717 (95% CI: 0.700–0.734) for grade 2, 0.750 (95% CI: 0.728–0.770) for grade 3, and 0.905 (95% CI: 0.873–0.930) for grade 4. The specificity was 0.876 (95% CI: 0.866–0.885) for grades 0 & 1, 0.862 (95% CI: 0.854–0.869) for grade 2, 0.972 (95% CI: 0.969–0.975) for grade 3, and 0.995 (95% CI: 0.993–0.996) for grade 4, demonstrating excellent discriminatory performance in distinguishing normal subjects from patients with osteoarthritis.

- For joint space narrowing (JSN) ≥ OARSI grade 1, the sensitivity and specificity were 0.880 (95% CI: 0.871–0.889) and 0.864 (95% CI: 0.860–0.869), respectively, demonstrating consistent and accurate detection of structural changes.
- For osteophyte detection ≥ OARSI grade 1, the sensitivity was 0.897 (95% CI: 0.888–0.905) and the specificity was 0.823 (95% CI: 0.813–0.833), indicating robust performance across varying severity levels.
- For sclerosis classification (presence/absence), the sensitivity and specificity were 0.88 (95% CI: 0.87–0.89) and 0.90 (95% CI: 0.89–0.90), respectively, demonstrating a high level of agreement with the reference standard.

Overall, MediAI-OA demonstrated accurate and reliable assessment of radiographic features of knee osteoarthritis, with clinical performance comparable to results obtained from annotations manually performed by qualified annotators who were rigorously trained in accordance with the OAI (Osteoarthritis Initiative) Standardized Reading Protocol.

# • Conclusion

Based on the non-clinical and clinical evidence, MediAI-OA is substantially equivalent to the legally marketed predicate device in terms of safety, effectiveness, and performance. The device introduces no new risks and demonstrates comparable functionality in aiding medical professionals in the assessment of radiographic features of knee osteoarthritis, supporting its suitability for regulatory clearance.

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**Source:** [https://fda.innolitics.com/device/K253172](https://fda.innolitics.com/device/K253172)

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