(133 days)
3D-Cut is intended to be used as a surgical instrument to assist in preoperative planning and/or in guiding the marking of bone and/or in guiding surgical instruments in non-acute, non-joint replacing ostector of bone tumors, for femur, tibia and pelvis including sacrum.
3D-Cut is a patient-matched additively manufactured single use surgical instrument (PS). Based on a preoperative planning, the instruments are intended to assist physicians in guiding the marking of bone and guiding surgical instruments in bone tumor resection surgery, excluding joint replacement surgeries.
The 3D-Cut instruments are designed starting from patient medical images, computed tomography (CT) and magnetic resonance imaging (MRI) device. The clinician delineates the tumor on the MRI. MRI and the delineated tumor are merged onto the CT which is used to extract the 3D CAD model of the bone. A draft treatment plan is submitted for evaluation to the treating clinician. Upon surgeon's approval, a PSI is designed and again submitted to the clinician. After validation, the PSI is produced using additive manufacturing.
The provided text describes the 3D-Cut device and its 510(k) submission, focusing on regulatory aspects, indications for use, and a high-level summary of performance data. However, this document does not contain the detailed information necessary to fully answer the specific questions regarding acceptance criteria, sample sizes, expert qualifications, ground truth establishment, or clinical study specifics like MRMC study results or effect sizes.
The text states: "Several tests have been conducted to demonstrate the output of the manufacturing process conforms to the device specifications. A combination of bench, cadaveric and clinical (OUS published case series) testing was executed to demonstrate the subject device is substantially equivalent to the predicate device and performs in accordance with its intended use." It also mentions "Software verification and validation were performed, and documentation was included in this submission in accordance with FDA Guidance 'Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices'".
This indicates that internal performance specifications and software verification/validation were performed, but the document does not elaborate on the specific acceptance criteria for these tests, nor does it provide details of a clinical study that would assess algorithm performance in the way suggested by the questions (e.g., number of experts, adjudication methods, MRMC studies, standalone performance data).
Therefore, I can only provide an answer that reflects the absence of the requested detailed information in the provided document.
Here's an assessment based on the provided document, highlighting the missing information:
1. A table of acceptance criteria and the reported device performance
The document mentions that "Several tests have been conducted to demonstrate the output of the manufacturing process conforms to the device specifications." and "Software verification and validation were performed". However, the specific acceptance criteria and the quantitative reported device performance for these tests are NOT provided in this document.
2. Sample sizes used for the test set and the data provenance
The document refers to "bench, cadaveric and clinical (OUS published case series) testing."
- Bench and Cadaveric Testing: No sample sizes are specified.
- Clinical Testing ("OUS published case series"): No sample size for the "case series" is provided, nor are details about the data provenance (e.g., specific country of origin, retrospective or prospective nature of these case series).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The document states that the "clinician delineates the tumor on the MRI" and a "draft treatment plan is submitted for evaluation to the treating clinician. Upon surgeon's approval, a PSI is designed and again submitted to the clinician. After validation, the PSI is produced." This implies clinical input for planning, but it does not specify the number of experts, their qualifications, or how a 'ground truth' for evaluating the device's performance (e.g., guiding surgery accuracy) was established for a test set. The clinical "validation" mentioned likely refers to the surgeon's approval of the design for a specific patient, not a generalized ground truth for a test set.
4. Adjudication method for the test set
No information on adjudication methods for a test set is provided.
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
There is no mention of an MRMC comparative effectiveness study, nor any data on how human readers (or surgeons in this context) improve with or without AI (device) assistance. The device is a physical surgical instrument resulting from preoperative planning, not explicitly an AI diagnostic tool for image interpretation.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The device is a "patient-matched additively manufactured single use surgical instrument (PSI)" based on "preoperative planning." The planning process involves a "clinician delineat[ing] the tumor on the MRI" and approving the treatment plan and PSI design. This indicates a human-in-the-loop process. No standalone algorithm performance data without human input is mentioned or applicable given the nature of the device.
7. The type of ground truth used
For the design and approval process, the "treating clinician's approval" and "validation" serves as the ground truth for shaping the PSI. For the performance of the manufactured device, "dimensional stability," "mechanical testing," and "simulated use testing on cadaveric specimen" would rely on physical measurements and surgical outcomes on cadavers. The "OUS published case series" would likely rely on clinical outcomes. However, a specific, generalized "ground truth" definition for a test set that would validate the device's accuracy in a structured study format (e.g., expert consensus on image interpretation, pathology, or long-term patient outcomes for a large cohort) is not described.
8. The sample size for the training set
The document describes a custom manufacturing process where the device is "designed starting from patient medical images." This implies a patient-specific design, not a general algorithm that is "trained" on a large dataset in the typical sense of machine learning. While there might be internal design rules or algorithms, the concept of a "training set" as understood in a machine learning context for diagnostic AI is not explicitly described or applicable in the provided information about this custom-manufactured surgical instrument.
9. How the ground truth for the training set was established
As there is no described "training set" in the context of an AI algorithm, this question is not applicable based on the provided text. The "ground truth" for the device's design is the clinician's approval of the proposed plan and PSI.
§ 888.3030 Single/multiple component metallic bone fixation appliances and accessories.
(a)
Identification. Single/multiple component metallic bone fixation appliances and accessories are devices intended to be implanted consisting of one or more metallic components and their metallic fasteners. The devices contain a plate, a nail/plate combination, or a blade/plate combination that are made of alloys, such as cobalt-chromium-molybdenum, stainless steel, and titanium, that are intended to be held in position with fasteners, such as screws and nails, or bolts, nuts, and washers. These devices are used for fixation of fractures of the proximal or distal end of long bones, such as intracapsular, intertrochanteric, intercervical, supracondylar, or condylar fractures of the femur; for fusion of a joint; or for surgical procedures that involve cutting a bone. The devices may be implanted or attached through the skin so that a pulling force (traction) may be applied to the skeletal system.(b)
Classification. Class II.