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510(k) Data Aggregation
(15 days)
The Quantum DiRex System provides diagnostic quality images to aid the physician with diagnosis. The DiRex can be used to perform radiographic exposures of the skeleton (including skull, spinal column and extremities), chest, abdomen and other body parts. The DiRex is not indicated for use in mammography.
The Quantum DiRex System is an integrated digital imaging system that combines the currently marketed Quantum Q-Rad Radiographic System with the currently marketed Agfa DX-S CR System (digitizer with NX workstation). The Quantum DiRex System is a combination of these previously cleared systems that have been combined and will be marketed as a single system.
Here's an analysis of the provided text regarding the acceptance criteria and study for the Quantum DiRex System:
Based on the provided text, the Quantum DiRex System is an integrated digital imaging system that combines the Quantum Q-Rad Radiographic System with the Agfa DX-S CR System. The primary acceptance criteria for this submission revolve around demonstrating substantial equivalence to predicate devices and meeting pre-determined performance criteria.
Here's a breakdown of the requested information:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Substantial Equivalence to Predicate Devices | "The Quantum DiRex System is substantially equivalent to the predicate devices and/or met pre-determined acceptance criteria." |
| Meet Pre-determined Performance Criteria | "Performance data demonstrated that the Quantum DiRex System... met pre-determined acceptance criteria." and "Performance testing was successfully completed on the proposed system in accordance with predetermined protocols based on the system design inputs." |
| Acceptable Risks | "The risks associated with use of the new device were found acceptable when evaluated by standardized risk/hazard analysis techniques." |
| Biocompatibility | "No biocompatibility testing was conducted... all patient-contacting materials... have been previously cleared for similar devices." (No new testing required as materials were pre-cleared) |
| Technological Characteristics Identity | "The technological characteristics are the same in the proposed and predicate devices." |
| Safety and Effectiveness Confirmation | "The Quantum DiRex System meets all the pre-determined acceptance criteria of the testing performed to confirm safety and effectiveness..." |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document does not specify a sample size for any test set or data provenance. The descriptions are general statements about "performance data" and "performance testing."
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The document does not mention any expert review committee, experts used to establish ground truth, or their qualifications. The testing appears to be focused on technical performance and equivalence, rather than clinical efficacy studies requiring expert reader consensus on diagnostic images.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
The document does not mention any adjudication method.
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
No, an MRMC comparative effectiveness study was not conducted. This submission is for a digital X-ray system, not an AI-powered diagnostic device, hence, there is no mention of human readers improving with AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
This question is not applicable as the device is a medical imaging system (hardware and associated software for image acquisition), not a standalone diagnostic algorithm. The performance described relates to the system's ability to produce diagnostic quality images, not an automated reading of those images.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The document does not explicitly state the type of ground truth used. Given the nature of the device (an imaging system, not a diagnostic algorithm), the "performance testing" likely involved evaluating image quality metrics, dose, system functionality, and compliance with pre-determined technical specifications (e.g., spatial resolution, contrast resolution, noise, consistency) rather than clinical ground truth diagnoses.
8. The sample size for the training set
The document does not mention a training set sample size. This is expected as the device is an imaging system, not a machine learning model that requires a training set.
9. How the ground truth for the training set was established
This question is not applicable as the device is an imaging system and not an AI/ML model requiring a training set with established ground truth.
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(204 days)
The Profile 1064 Laser Systems and Accessories are intended for use in the medical specialties of general and plastic surgery, dermatology, endoscopic/laproscopic general surgery, gastroenterology, gynecology, otorhinolaryngology (ENT), neurosurgery, oculoplastics, orthopedics, pulmonary/thoracic surgery and urology for surgical and aesthetic applications.
Dermatology:
Coagulation and hemostasis of benign vascular lesions such as, but not limited to port wine stains, hemangiomas, warts, telangiectasia, rosacea, venus lake, leg veins and spider veins. The lasers are also intended for the treatment of benign pigmented lesions such as, but not limited to, lentigos (age spots), solar lentigos (sun spots), café au lait macules, seborrheic keratoses, nevi, chloasma, verrucae, skin tags, keratoses, tattoos (significant reduction in the intensity of black and/or blue/black tattoos) and plaques. Additionally, the lasers are indicated for pigmented lesions to reduce lesion size, for patients with lesions that would potentially benefit from aggressive treatment, and for patients with lesions that have not responded to other laser treatments.
Reduction of red pigmentation in hypertrophic and keloid scars where vascularity is an integral part of the scar.
Removal of unwanted hair, for the stable long term, or permanent hair reduction through selective targeting of melanin in hair follicles, and for the treatment for pseudofolliculitis barbae (PFB). The Profile 1064 Laser Systems and Accessories are indicated for use on all skin types (Fitzpatrick I-VI), including tanned skin.
The Profile 1064 Laser Systems and Accessories are indicated for the treatment of facial wrinkles.
The intended use of the contact cooling system in the Profile handpiece is to provide cooling of the skin prior to, during and after laser treatment, for the epidermal protection and reduction of pain during laser treatment, to allow for the use of higher fluences for laser treatments such as hair removal and vascular lesions, and to reduce the potential side effects of laser treatments.
Surgical Applications:
Incision/excision and cutting, ablation, coagulation/hemostasis of soft tissue in the performance of surgical applications in endoscopy/laparoscopy, gastroenterology, general surgery, head and neck/otorhinolaryngology (ENT), neurosurgery, oculoplastics, orthopedics, plastic surgery, pulmonary/thoracic surgery, gynecology (e.g. menorrhagia) and urology.
Profile Laser System is an Nd:YAG laser producing emission at a wavelength of 1064nm. It consists of a laser console, internal computer, control panel and display, an optical delivery system comprised of an articulated arm and a handpiece or scanner with cooling capability, and a footswitch.
The provided text describes a 510(k) submission for the Sciton Profile 1064 Laser System, demonstrating its substantial equivalence to previously marketed predicate devices. This type of submission generally relies on demonstrating equivalence rather than conducting new clinical studies with defined acceptance criteria and performance metrics.
Therefore, this document does not contain the information requested in points 1, 2, 3, 4, 5, 6, 7, 8, and 9 as it is a regulatory submission focused on substantial equivalence rather than a study report detailing specific performance criteria, test sets, or training sets for an AI/ML device.
Here's why the information is not present based on the provided text:
- Acceptance Criteria and Reported Device Performance (1): The submission focuses on substantial equivalence to predicate devices, meaning its performance is considered acceptable if it is similar to those already on the market. There are no explicit acceptance criteria or a quantitative performance table mentioned.
- Sample Size, Data Provenance (2): No new clinical study data with test sets, sample sizes, or data provenance is presented. The argument is based on the similarity of the device's technological characteristics and intended use to existing devices.
- Experts, Qualifications, Adjudication (3, 4): These details are relevant for studies establishing ground truth, which is not the primary mechanism of this 510(k) submission.
- MRMC Study (5): The submission does not mention an MRMC study or any assessment of human reader improvement with AI assistance, as the device is a laser system, not an AI-assisted diagnostic tool.
- Standalone Performance (6): Similar to the above, this concept applies to AI/ML devices, not a laser system.
- Type of Ground Truth (7): Ground truth is not established in this context as new clinical validation is not the basis of the submission.
- Training Set Sample Size/Ground Truth (8, 9): These concepts are applicable to AI/ML model development. This submission is for a physical medical device (laser system), not an AI algorithm.
In summary, the provided 510(k) summary focuses on demonstrating that the "Profile 1064 Laser System" is substantially equivalent to other legally marketed Nd:YAG laser systems based on shared indications for use and technological characteristics. It is not a report of a study designed to establish new performance criteria or to validate an AI/ML algorithm against a ground truth dataset.
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