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TechCare Trauma is intended to analyze 2D X ray radiographs using techniques to aid in the detection, localization, and characterization of fractures and/or elbow joint effusion during the review of commonly acquired radiographs of: Ankle, Foot, Knee, Leg (includes Tibia/Fibula), Femur, Wrist, Hand/Finger, Elbow, Forearm, Arm (includes Humerus), Shoulder, Clavicle, Pelvis, Hip, Thorax (includes ribs).

TechCare Trauma can provide results for fracture in neonates and infants (from birth to less than 2 years), children and adolescents (aged 2 to less than 22 years) and adults (aged 22 years and over).

TechCare Trauma can provide results for elbow joint effusions in children and adolescents (aged 2 to less than 22 years) and adults (aged 22 years and over).

The intended users of TechCare Trauma are clinicians with the authority to diagnose fractures and/or elbow joint effusions in various settings including primary care (e. g., family practice, internal medicine), emergency medicine, urgent care, and specialty care (e. g. orthopedics), as well as radiologists who review radiographs across settings.

TechCare Trauma results are not intended to be used on a stand-alone basis for clinical decision-making. Primary diagnostic and patient management decisions are made by the clinical user.

The TechCare Trauma device is a software as Medical Device (SaMD). More specifically it is defined as a "radiological computer assisted detection and diagnostic software for suspected fractures".

As a CADe/x software, TechCare Trauma is an image processing device intended to aid in the detection and localization of fractures and elbow joint effusions on acquired medical images (2D X-ray radiographs).

TechCare Trauma uses an artificial intelligence algorithm to analyze acquired medical images (2D X-ray radiographs) for features suggestive of fractures and elbow joint effusions.

TechCare Trauma can provide results for fractures in neonates and infants (from birth to less than 2 years), children and adolescents (aged 2 to less than 22 years) and adults (aged 22 years and over) regardless of their condition.

TechCare Trauma can provide results for elbow joint effusions in children and adolescents (aged 2 to less than 22 years) and adults (aged 22 years and over).The device detects and identifies fractures and elbow joint effusions based on a visual model's analysis of images and provides information about the presence and location of these prespecified findings to the user.

It relies solely on images provided by DICOM sources. Once integrated into existing networks, TechCare Trauma automatically receives and processes these images without any manual intervention. The processed results, which consist of one or more images derived from the original inputs, are then sent to specified DICOM destinations. This ensures that the results can be seamlessly viewed on any compatible DICOM viewer, allowing smooth into medical imaging workflows.

TechCare Trauma can be deployed on-premises or on cloud and be connected to multiple DICOM sources / destinations (including but not limited to DICOM storage platform, PACS, VNA and radiological equipment, such as X-ray systems), ensuring easy integration into existing clinical workflows.

Here's a detailed breakdown of the acceptance criteria and study findings for the TechCare Trauma device, based on the provided text:

Acceptance Criteria and Device Performance

The acceptance criteria for the TechCare Trauma device appear to be based on achieving high diagnostic accuracy, specifically measured by the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve for both standalone performance and multi-reader multi-case (MRMC) comparative studies. The study demonstrated successful performance against these implied criteria.

Table of Acceptance Criteria and Reported Device Performance

Study Details

2. Sample sizes used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Standalone Performance Test Set:

• Fracture Detection: 4109 radiographs of US adult patients and 2872 radiographs of US pediatric patients.
• EJE Detection: 280 radiographs of US adult patients and 483 radiographs of US pediatric patients.
• Data Provenance: Retrospective, obtained from various states in the US (at least 4) and various imaging hardware manufacturers (at least 14).

MRMC Comparative Effectiveness Study Test Set:

• Adult US population for fracture detection: 304 radiological cases
• Pediatric US population for fracture detection: 256 radiological cases
• Adult US population for EJE detection: 109 radiological cases
• Pediatric US population for EJE detection: 100 radiological cases
• Data Provenance: Retrospective, external multicenter anonymized datasets obtained from sites that were different from the training data sites, ensuring independence. All data from US patients.

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

Standalone Performance Test Set:

• Number of Experts: Three American Board of Radiology (ABR)-certified radiologists for both adult and pediatric cases.
• Qualifications: Minimum of 5 years of experience since ABR certification. Pediatric cases were annotated by pediatric radiologists, and adult cases by musculoskeletal (MSK) radiologists.

MRMC Comparative Effectiveness Study Test Set:

• Number of Experts: Three ABR-certified radiologists.
• Qualifications: At least five years of experience. Pediatric cases were annotated by a panel of three pediatric radiologists, while adult cases were reviewed by a panel of three MSK radiologists.

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

Both Standalone and MRMC Studies:

• Adjudication Method: Two radiologists independently assessed each case. For cases with disagreement between the first two, a third radiologist independently reviewed the case. The final reference standard (ground truth) was determined by majority consensus (referred to as "2+1" if two agree, or "3+0" if all three agree after subsequent review).

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

• Yes, a MRMC comparative effectiveness study was done.

• Effect Size of Improvement (AI vs. without AI assistance):

  • Adult Fracture (ROC AUC delta): 0.090 (from 0.865 to 0.955)
  • Adult EJE (ROC AUC delta): 0.064 (from 0.851 to 0.914)
  • Pediatric Fracture (ROC AUC delta): 0.074 (from 0.857 to 0.931)
  • Pediatric EJE (ROC AUC delta): 0.063 (from 0.877 to 0.941)

  Additionally, significant improvements in sensitivity were observed:

  • Adult Fracture Sensitivity: +21.8%
  • Adult EJE Sensitivity: +12.7%
  • Pediatric Fracture Sensitivity: +19.9%
  • Pediatric EJE Sensitivity: +18.2%

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

• Yes, a standalone performance study was done.
  • Fracture Detection ROC-AUC: 0.962 (Adults) and 0.962 (Pediatrics)
  • EJE Detection ROC-AUC: 0.965 (Adults) and 0.976 (Pediatrics)

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

• The ground truth for both standalone and MRMC studies was established by expert consensus of ABR-certified radiologists.

8. The sample size for the training set

• Training Set Sample Size: 95,266 images.

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. However, given the detailed methodology for the test set ground truth, it is highly probable that a similar expert-driven annotation process (potentially internal and/or external) was followed for the training data as well. The text states the training was performed "from various manufacturers," suggesting a diverse dataset that would necessitate robust ground truthing.

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