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FractureDetect (FX) is a computer-assisted detection and diagnosis (CAD) software device to assist clinicians in detecting fractures during the review of radiographs of the musculoskeletal system. FX is indicated for adults only.

FX is indicated for radiographs of the following industry-standard radiographic views and study types.

| Study Type
(Anatomic Area
of Interest⁺) | Radiographic View(s)
Supported* |
|-----------------------------------------------|------------------------------------|
| Ankle | Frontal, Lateral, Oblique |
| Clavicle | Frontal |
| Elbow | Frontal, Lateral |
| Femur | Frontal, Lateral |
| Forearm | Frontal, Lateral |
| Hip | Frontal, Frog Leg Lateral |
| Humerus | Frontal, Lateral |
| Knee | Frontal, Lateral |
| Pelvis | Frontal |
| Shoulder | Frontal, Lateral, Axillary |
| Tibia / Fibula | Frontal, Lateral |
| Wrist | Frontal, Lateral, Oblique |

*For the purposes of this table, "Frontal" is considered inclusive of both posteroanterior (PA) and anteroposterior (AP) views.

+Definitions of anatomic area of interest and radiographic views are consistent with the American College of Radiology (ACR) standards and guidelines.

FractureDetect (FX) is a computer-assisted detection and diagnosis (CAD) software device designed to assist clinicians in detecting fractures during the review of commonly acquired adult radiographs. FX does this by analyzing radiographs and providing relevant annotations, assisting clinicians in the detection of fractures within their diagnostic process at the point of care. FX was developed using robust scientific principles and industry-standard deep learning algorithms for computer vision.

FX creates, as its output, a DICOM overlay with annotations indicating the presence or absence of fractures. If any fracture is detected by FX, the output overlay is composed to include the text annotation "Fracture: DETECTED" and to include one or more bounding boxes surrounding any fracture site(s). If no fracture is detected by FX, the output overlay is composed to include the text annotation "Fracture: NOT DETECTED" and no bounding box is included. Whether or not a fracture is detected, the overlay includes a text annotation identifying the radiograph as analyzed by FX and instructions for users to access labeling. The FX overlay can be toggled on or off by the clinicians within their PACS viewer, allowing for uninhibited concurrent review of the original radiograph.

Here's a detailed breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size:
  • Standalone Study: 11,970 radiographs.
  • MRMC Reader Study: 175 cases.
• Data Provenance: Not explicitly stated, but the experts establishing ground truth are specified as U.S. board-certified, suggesting the data is likely from the U.S. There is no indication whether the data was retrospective or prospective, but for an FDA submission of this nature, historical retrospective data is common.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

• Number of Experts: A panel of three experts was used for the MRMC study's ground truth.
• Qualifications: "U.S. board-certified orthopedic surgeons or U.S. board-certified radiologists." Specific years of experience are not mentioned.

4. Adjudication Method for the Test Set

• Adjudication Method: A "panel of three" experts assigned a ground truth binary label (presence or absence of fracture). This implies a consensus-based adjudication. While not explicitly stated (e.g., 2-out-of-3, or further adjudication if there was disagreement), the phrasing suggests a collective agreement to establish the "ground truth." This is analogous to a 3-expert consensus, where the majority rules.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? Yes.
• Effect Size (Improvement with AI vs. without AI assistance):
  • Readers' AUC significantly improved by 0.0406 (from 0.912 to 0.952).
  • Readers' sensitivity improved by 0.081 (from 0.819 to 0.900).
  • Readers' specificity improved by 0.028 (from 0.890 to 0.918).

6. Standalone (Algorithm Only) Performance Study

• Was a standalone study done? Yes.
• Performance:
  • Sensitivity: 0.951
  • Specificity: 0.893
  • Overall AUC: 0.982
  • High accuracy across study types and potential confounders (image brightness, x-ray manufacturers).

7. Type of Ground Truth Used

• Standalone Study: The ground truth for the standalone study is not explicitly detailed but given the MRMC study, it's highly probable it also leveraged expert consensus, similar to the MRMC setup, for fracture detection.
• MRMC Study: Expert Consensus by a panel of three U.S. board-certified orthopedic surgeons or U.S. board-certified radiologists.

8. Sample Size for the Training Set

• The document does not explicitly state the sample size for the training set. It only mentions "robust scientific principles and industry-standard deep learning algorithms for computer vision" were used for development.

9. How the Ground Truth for the Training Set Was Established

• The document does not explicitly describe how the ground truth for the training set was established. It only mentions "Supervised Deep Learning" as the methodology, which implies labeled data was used for training, but the process of obtaining these labels is not detailed.

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