(177 days)
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
| Metric | Acceptance Criteria (Implied/Study Goal) | Reported Device Performance (Standalone) | Reported Device Performance (MRMC with AI vs. without AI) |
|---|---|---|---|
| Standalone Performance (Image-level ROC-AUC) | High accuracy (specific threshold not explicitly stated but implied by achievement across all categories) | Fracture - Adult: 0.962 [0.957 - 0.967] | |
| Fracture - Pediatric: 0.962 [0.955 - 0.969] | |||
| EJE - Adult: 0.965 [0.936 - 0.986] | |||
| EJE - Pediatric: 0.976 [0.963 - 0.986] | |||
| (Further detailed by anatomical regions, age, gender, image view, and imaging hardware manufacturers, all showing high AUCs.) | Not applicable (standalone algorithm only) | ||
| Reader Performance (MRMC ROC-AUC) | Superior to unaided reader performance (statistically significant improvement) | Not applicable (human reader performance) | Adult Fracture: Improved from 0.865 to 0.955 (Δ 0.090, p |
§ 892.2090 Radiological computer-assisted detection and diagnosis software.
(a)
Identification. A radiological computer-assisted detection and diagnostic software is an image processing device intended to aid in the detection, localization, and characterization of fracture, lesions, or other disease-specific findings on acquired medical images (e.g., radiography, magnetic resonance, computed tomography). The device detects, identifies, and characterizes findings based on features or information extracted from images, and provides information about the presence, location, and characteristics of the findings to the user. The analysis is intended to inform the primary diagnostic and patient management decisions that are made by the clinical user. The device is not intended as a replacement for a complete clinician's review or their clinical judgment that takes into account other relevant information from the image or patient history.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis algorithm, including a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable.
(iii) Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contains a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in paragraph (b)(1)(iii) of this section.(v) Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures.
(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the device instructions for use, including the intended reading protocol and how the user should interpret the device output.
(iii) A detailed description of the intended user, and any user training materials or programs that address appropriate reading protocols for the device, to ensure that the end user is fully aware of how to interpret and apply the device output.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) A detailed summary of the performance testing, including test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.