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The MediAI-BA is designed to view and quantify bone age from 2D Posterior Anterior (PA) view of left-hand radiographs using deep learning techniques to aid in the analysis of bone age assessment of patients between 2 to 18 years old for pediatric radiologists. The results should not be relied upon alone by pediatric radiologists to make diagnostic decisions. The images shall be with left hand and wrist fully visible within the field of view, and shall be without any major bone destruction, deformity, fracture, excessive motion, or other major artifacts.

Limitations:

• This software is not intended for use in patients with growth disorders caused by congenital anomalies (e.g., Down syndrome, Noonan syndrome, congenital adrenal hyperplasia, methylmalonic acidemia, skeletal dysplasia, chronic renal disease, or prior long-term steroid exposure), as these conditions may cause complex skeletal changes beyond bone maturation.
• Images showing anatomical variations or notable abnormalities (e.g., bone tumors, sequelae of fractures, or congenital deformities) in the region required for interpretation are excluded from the intended use.

This AI-based software utilizes an internal algorithm that integrates global skeletal maturity features extracted from the whole hand radiograph with local skeletal maturity features derived from key Regions of Interest (ROIs). By synthesizing these skeletal maturity features, the software determines the accurate final bone age.

MediAI-BA provides an optional heatmap visualization that highlights regions contributing to the AI model output. The heatmap is intended only as supplementary, qualitative information to illustrate internal AI operations and is not intended for clinical interpretation, growth plate localization, or independent bone age assessment.

The confidence score graph is an internal model visualization intended only to illustrate the relative sharpness of the model's output distribution. It is not calibrated to clinical likelihood, has not been clinically validated, and is not intended to support diagnostic decisions or selection of a specific bone age.

Here's a breakdown of the acceptance criteria and study details for the MediAI-BA device, based on the provided FDA 510(k) clearance letter:

MediAI-BA Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance:

Study Details

2. Sample size used for the test set and the data provenance:

• Sample Size: 600 cases.
• Data Provenance:
  • Country of Origin: United States.
  • Collection Sites: Five sites across multiple states and multiple clinical organizations.
  • Retrospective/Prospective: Not explicitly stated, but the description of "collected from five sites" suggests a retrospective collection of existing images for this study. The phrase "None of the cases used in this study were utilized for training or development of the MediAI-BA model" reinforces that these were untouched test cases.
  • Demographics: 50.0% males and 50.0% females. Racial/ethnic composition included White, Hispanic, Black, Asian & Pacific Islander, among others.
  • Image Sources: X-ray scanner manufacturers included Samsung Electronics, Carestream Health, Kodak, Siemens, and Konica Minolta.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of Experts: Four evaluators.
• Qualifications of Experts: Not explicitly stated, but the context of "pediatric radiologists" in the Indications for Use and the assertion that the device "demonstrated performance comparable to bone age readings obtained by human evaluators using the GP atlas method" strongly imply that these evaluators were pediatric radiologists experienced in bone age assessment using the GP (Greulich and Pyle) atlas method.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• The document states that ground truth was "established by four evaluators." It does not specify the exact adjudication method (e.g., whether it was consensus, average, or majority rule among the four).

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No, an MRMC comparative effectiveness study was not explicitly described. The study compared the device's standalone performance against the ground truth established by human evaluators. It did not evaluate how human readers' performance might improve when assisted by the AI.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:

• Yes, a standalone performance study was done. The performance metrics (Deming regression, Bland-Altman, frequency distribution of differences) directly compare the "software's bone age analysis results" and "MediAI-BA outputs" against the "ground truth." This is a direct measurement of the algorithm's standalone performance.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

• The ground truth was established by "four evaluators" using the "GP atlas method." This indicates expert consensus/interpretation using a recognized standard (GP atlas).

8. The sample size for the training set:

• Not specified in the provided text. The document explicitly states that "None of the cases used in this study were utilized for training or development of the MediAI-BA model," but does not give details about the training set itself.

9. How the ground truth for the training set was established:

• Not specified in the provided text.

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