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MyEllevate is indicated for use in soft tissue approximation of sub dermis and underlying muscle

The MyEllevate device consists of a Surgical Suture System consisting of synthetic non-absorbable, braided, polyester surgical suture, and a set of instruments designed to efficiently and accurately place the suture for the purpose of performing soft tissue approximation. MyEllevate contains a Skin Marking Tape, a Lancet, a Tunneling Rod, two stainless steel Suturods, and a Light Handle which contains an LED light source. Each Suturod™ has an internal fiber optic "light pipe" which, when connected to the Ligh Handle, illuminates the distal tip of the Suturod™ and permits visual verification of the location and depth of the Suturod™ as it is passed through the subcutaneous space. All components of MyEllevate are provided sterile and are intended for single use only. While the nonabsorbable suture is a Class II device as describes by 21CFR, §878.5000, the remaining instruments are Class I devices exempt from 510(k) requirements. The Lancet, Clearing Device, and Tunneling Rod are manual surgical devices described by 21 CFR §878.4800. The Skin Marking Pen is described in 21 CFR §878.4660, and the fiber optic "light pipe" and Light Handle to illuminate surgical sites, is described in 21 CFR, §876.4530.

The provided document is a 510(k) summary for the MyEllevate device, which is a surgical suture system. This document is a premarket notification for a medical device and therefore does not contain information about an AI/ML-based device.

The document explicitly states:

• "807.92(b)(2) Clinical tests submitted – N/A – No clinical tests submitted" (Page 6), indicating no human performance or clinical studies were conducted for this submission.
• The device being cleared is a "Nonabsorbable Polyester Suture" and associated manual surgical instruments (Suturods, Lancet, Tunneling Rod, Light Handle, Skin Marking Tape). There is no mention of any AI or machine learning component.

Therefore, I cannot extract the requested information regarding acceptance criteria and a study proving an AI/ML device meets these criteria because the submitted text is a 510(k) for a physical medical device (suture system), not an AI/ML device.

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Accuracy In Marking® is a marking device used in conjunction with a transducer during vascular mapping. The device allows the user to mark a vascular area immediately when it is identified during mapping process. The guide path underneath the transducer allows the user to identify the target vessel prior to marking. The product is supplied non-sterile and is intended for single use only.

The marking device is enables the sonographer to mark the "targeted" vessel during vascular mapping. The shadow guide is an adhesive "film" adhered directly underneath the footing of the transducer used as guide on identifying the vessel. The marking device and the "shadow guide" are attached to a transducer using a non-latex adhesive. The device shall be sold non-sterile for single use only. This device is compatible with FDA approved linear and curved linear ultrasonic transducers in the frequency of 4MHz to 12MHz.

The provided 510(k) submission for the "AiM Accuracy In Marking® System" (K053463) does not contain a detailed study report with specific acceptance criteria and performance data in the format typically required for advanced AI/ML medical devices.

Instead, the submission focuses on demonstrating substantial equivalence to a predicate device (Sandel Medical Industries, LLC Skin Marker) based on similarities in function (skin marking) and the simplicity of the "shadow guide" component. The primary "test data" mentioned is to demonstrate that the safety and effectiveness of the transducer's function is not affected by the shadow guide.

Therefore, many of the requested elements for describing acceptance criteria and study details cannot be extracted from this document. However, I can infer what information would be relevant if such a study were present, and provide the limited details available.

Based on the provided document, here's an attempt to answer your questions:

1. A table of acceptance criteria and the reported device performance

• Acceptance Criteria: The document does not explicitly state quantitative acceptance criteria for the "AiM Accuracy In Marking® System." The primary "acceptance" seems to be based on demonstrating that the shadow guide does not adversely affect the safety and effectiveness of the existing ultrasound transducer function, rather than achieving a specific performance metric for aiding vessel marking. The core claim is that its function is "beneficial on identifying the target vessel."
• Reported Device Performance: The document only makes a qualitative statement: "the simplicity of the design as well as test data demonstrates that safety and effectiveness of the function of the transducer is not affected by the shadow guide. The shadow guide's function is beneficial on identifying the target vessel during vascular mapping."

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

• Sample Size: Not specified. The document refers generally to "test data," but does not provide any numerical sample size for cases or participants.
• Data Provenance: Not specified. While the applicant's address is in California, USA, the origin of any "test data" (e.g., country, institution) is not mentioned.
• Retrospective or Prospective: Not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The document does not detail how "ground truth" for beneficial vessel identification was established or if any expert evaluation was involved beyond general statements.

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

• Adjudication Method: Not specified. Given the lack of detail on the "test data," no adjudication method is described.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not described or referenced. This device is a passive marking accessory, not an AI/ML algorithm requiring a human-in-the-loop performance evaluation in the typical sense.
• Effect Size: Not applicable, as no MRMC study was presented.

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

• Standalone Performance: Not applicable. This device is a physical accessory (shadow guide and marker), not a standalone algorithm. Its function is to be used with a human sonographer and ultrasound transducer.

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

• Type of Ground Truth: Not explicitly stated. Based on the description, the "ground truth" for the device's function (identifying target vessels) would likely be based on the sonographer's visual confirmation of the vessel on the ultrasound image and their ability to accurately mark it based on the guide. However, the exact method for establishing this is not detailed in the submission.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This is a physical accessory, not an AI/ML device that requires a training set.

9. How the ground truth for the training set was established

• Ground Truth for Training Set: Not applicable. This is a physical accessory, not an AI/ML device.

Summary Limitations:

The provided 510(k) summary is for a relatively simple accessory device. The requirements for demonstrating substantial equivalence for such devices are often less stringent regarding quantitative performance studies compared to complex AI/ML algorithms. The focus here is on demonstrating that the accessory does not impair the functionality or safety of the primary device (ultrasound transducer) and that its stated purpose (aiding identification and marking) is met in a general sense, without specific numerical performance targets or detailed study methodology being disclosed in this summary.

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The SURGICHIP Tag is intended to mark an anatomical site for surgery.

The SURGICHIP Tag surgical marker is indicated to mark an anatomical site for surgery.

The SURGICHIP™ Tag smart label and procedure that uses Radio Frequency Identification technology to mark an anatomical site prior to surgery. It is designed to be used in addition to the usual safeguards so that the correct procedure is performed on the correct site and on the intended patient.

The provided text describes a 510(k) submission for the SURGICHIP™ Tag Surgical Marker. However, it does not contain the detailed information required to fill out a table of acceptance criteria and reported device performance, nor does it describe a study with the specific parameters requested.

The document primarily focuses on:

• Device Description: The SURGICHIP™ Tag uses Radio Frequency Identification (RFID) to mark an anatomical site prior to surgery, supplementing existing safeguards to ensure correct procedure, site, and patient.
• Intended Use/Indications for Use: To mark an anatomical site for surgery.
• Substantial Equivalence: The device is deemed substantially equivalent to a Skin Marker, an Adhesive Surgical Site Marker, and a RFID Hospital Identification Wristband. The document states, "Testing of the subject device demonstrated that the SURGICHIP Tag is an effective method of marking a surgical site prior to surgery and is equivalent to the predicate devices."

Missing Information:

The document does not provide:

• Specific acceptance criteria (e.g., quantitative performance metrics, thresholds for accuracy, reliability, etc.).
• Any details about the "testing" mentioned, such as study design, methodology, sample sizes, data provenance, ground truth establishment, expert involvement, or statistical analysis.
• Results in a format suitable for a "reported device performance" section with specific metrics.
• Information about multi-reader multi-case (MRMC) comparative effectiveness studies or standalone algorithm performance.
• Details about training sets if an AI/algorithm-based device were involved (which, based on the description, is not the primary function of this RFID tag).

Conclusion:

Based on the provided text, it's not possible to generate the requested table or describe the study in detail because the document is a 510(k) summary focused on substantial equivalence rather than a detailed report of a performance study with specific acceptance criteria and results. The "testing" mentioned is referred to only in a broad statement, without any supporting data or methodology.

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The Thermage ThermaCool TC System (Model TC) is indicated for use in:

Dermatologic and general surgical procedures for electrocoagulation and hemostasis,

Non-invasive treatment of periorbital wrinkles and rhytids

The Thermage ThermaCool TC System consists of the following components:

• RF Generator
• Cooling Module
• Cryogen Canister
• Handpiece Assembly (consisting of Handpiece and Treatment Tip)
• Accessory cables and tubing
• Optional footswitch component
• Accessories: coupling fluid, return pad and skin marking paper

The Handpiece Assembly and Cooling Module connect to the RF Generator.

I am sorry, but based on the provided text, there is no information about acceptance criteria, device performance metrics, or any studies conducted to prove that the device meets specific criteria.

The document provided is a 510(k) premarket notification summary for the Thermage ThermaCool TC System. It describes the device, its intended use, and its substantial equivalence to previously cleared predicate devices. It also includes the FDA's clearance letter.

This type of document typically focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than presenting detailed performance acceptance criteria and study results in the manner you described for an AI/ML device. The "technological characteristics and clinical use data" mentioned are used to establish substantial equivalence to predicate devices, but the specifics of any performance evaluations or clinical trials for this particular device (beyond what's needed for the 510(k) process) are not detailed in this excerpt.

Therefore, I cannot fulfill your request to provide a table of acceptance criteria and reported device performance or details about study design elements like sample size, data provenance, expert adjudication, MRMC studies, standalone performance, ground truth sources, or training set information.

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The Thermage ThermaCool TC System is indicated for use in Dermatologic and General Surgical procedures for electrocoagulation and hemostasis.

The Thermage ThermaCool TC System consists of the following components:

• RF Generator .
• Coolant Control Center ●
• Coolant Canister .
• Handpiece Assembly (consisting of Handpiece and Treatment Tip) .
• Accessory cables and tubing ●
• Optional footswitch component .
• Accessories: coupling fluid, return pad and skin marking paper .

The Handpiece Assembly and Coolant Control Center connect to the RF Generator.

Here's a breakdown of the requested information based on the provided text, outlining the acceptance criteria and study details for the Thermage ThermaCool TC System:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not contain explicit acceptance criteria (e.g., target accuracy, sensitivity, specificity, or specific performance metrics) that the Thermage ThermaCool TC System had to meet. Instead, the submission relies on the concept of substantial equivalence to predicate devices. Therefore, the "acceptance criteria" are implicitly met by demonstrating that the new device has "substantially equivalent" technological characteristics and intended use to previously cleared devices.

2. Sample Size Used for the Test Set and Data Provenance

The provided 510(k) summary does not describe a clinical study with a "test set" in the traditional sense of evaluating diagnostic performance. The submission relies on establishing substantial equivalence through a comparison of technological characteristics and intended use with predicate devices. Therefore, there is no mention of a specific sample size for a test set, nor is there information about data provenance (e.g., country of origin, retrospective/prospective).

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

As there is no described clinical study with a "test set" or diagnostic performance evaluation, there is no mention of experts used to establish ground truth or their qualifications.

4. Adjudication Method for the Test Set

Since no test set or diagnostic performance study is described, there is no information about an adjudication method.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study is not mentioned in the provided document. The submission focuses on substantial equivalence rather than comparative effectiveness with human readers.

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

This device is an electrosurgical unit, not an AI algorithm. Therefore, the concept of a "standalone (algorithm only)" performance study is not applicable. The submission focuses on the safety and effectiveness of the medical device itself through substantial equivalence.

7. The Type of Ground Truth Used

Given that this is a 510(k) submission for an electrosurgical unit based on substantial equivalence, the "ground truth" is effectively derived from the established safety and effectiveness of the predicate devices. There is no mention of pathology, outcomes data, or expert consensus in the context of demonstrating this device's performance against a specific "ground truth" for a diagnostic task.

8. The Sample Size for the Training Set

This submission is for a medical device (electrosurgical unit), not an AI/machine learning algorithm. Therefore, the concept of a "training set" in the context of machine learning does not apply.

9. How the Ground Truth for the Training Set Was Established

As this is not an AI/machine learning device, the concept of establishing "ground truth for a training set" is not applicable.

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