(98 days)
Limbus Contour is a software-only medical device intended for use by trained radiation oncologists, dosimetrists and physicists to derive optimal contours for input to radiation treatment planning. Supported image modalities are Computed Tomography and Magnetic Resonance. The Limbus Contour Software assists in the following scenarios:
· Operates in conjunction with radiation treatment planning systems or DICOM viewing systems to load, save, and display medical images and contours for treatment evaluation and treatment planning.
· Creation, transformation, and modification of contours for applications including, but not limited to: transferring contours to radiotherapy treatment planning systems, aiding adaptive therapy and archiving contours for patient follow-up.
· Localization and definition of healthy anatomical structures.
Limbus Contour is not intended for use with digital mammography.
Limbus Contour is not intended to automatically contour tumor clinical target volumes.
Limbus Contour is a stand-alone software medical device. It is a single purposes cross-platform application for automatic contouring (segmentation) of CT/MRI DICOM images via pre-trained and expert curated machine learning models. The software is intended to be used by trained medical professionals to derive contours for input to radiation treatment planning. The Limbus Contour software segments normal tissues using models and further post-processing on machine learning model prediction outputs. Limbus Contour does not display or store DICOM images and relies on existing radiotherapy treatment planning systems (TPS) and DICOM image viewers for display and modification of generated segmentations. Limbus Contour interfaces with the user's operating system file system (importing DICOM image .dcm files and exporting segmented DICOM RT-Structure Set .dcm files).
Here's a breakdown of the acceptance criteria and study information for the Limbus Contour device based on the provided FDA 510(k) summary:
Acceptance Criteria and Device Performance
The document does not explicitly state a table of "acceptance criteria" with numerical targets and reported performance in a pass/fail format. Instead, it refers to validation testing to demonstrate that the software meets "user needs and intended uses" and performs "in accordance with specifications." The implicit acceptance is that the automatic contouring function is accurate and comparable to the predicate device.
However, based on the general context of premarket submissions, the underlying acceptance criteria for automatic contouring typical involves metrics related to overlap, distance, and shape similarity. Since specific numerical metrics and targets are not provided, I will infer the successful demonstration of functionality as the reported device performance.
| Acceptance Criteria Category | Reported Device Performance |
|---|---|
| Automatic Contouring | Validation testing demonstrated that the software meets user needs and intended uses for automatic contouring and performs in accordance with specifications. Performance is comparable to the predicate device. |
Study Details
2. Sample Size for Test Set and Data Provenance
The document does not specify the exact sample size (number of cases or patients) used for the test set. It mentions "Validation testing of the following functions of the Limbus Contour application demonstrated that the software meets user needs and intended uses and to support substantial equivalence: Automatic Contouring – Validation Test."
The data provenance (economic area, retrospective/prospective) is also not specified in the provided text.
3. Number of Experts and Qualifications for Ground Truth
The document does not specify the number of experts or their qualifications used to establish the ground truth for the test set. It mentions "pre-trained and expert curated machine learning models" in the device description, implying expert involvement in the training data, but does not explicitly describe it for the test set.
4. Adjudication Method
The document does not specify any adjudication method (e.g., 2+1, 3+1) used for establishing the ground truth of the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was mentioned, nor was any effect size of human readers improving with AI assistance reported. The study focused on the standalone performance of the device and its substantial equivalence to a predicate.
6. Standalone Performance Study
Yes, a standalone (algorithm only without human-in-the-loop performance) study was performed. The document explicitly states: "Validation testing of the following functions of the Limbus Contour application demonstrated that the software meets user needs and intended uses and to support substantial equivalence: Automatic Contouring – Validation Test." This is a validation of the autonomous contouring capability.
7. Type of Ground Truth Used
The document does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data) for the test set. The device description mentions "expert curated machine learning models," which suggests expert annotations were used at some stage, likely for training. However, the exact nature of the ground truth for the independent validation (test) set is not detailed.
8. Sample Size for Training Set
The document does not specify the sample size used for the training set.
9. How Ground Truth for Training Set was Established
The device description states that the Limbus Contour operates via "pre-trained and expert curated machine learning models." This indicates that the ground truth for the training set was established through expert curation, meaning medical experts (likely radiation oncologists, dosimetrists, or physicists) manually contoured structures which were then used to train the machine learning models.
§ 892.2050 Medical image management and processing system.
(a)
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.(b)
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).