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Detect is an electronic device used to indicate the location of the apex and the working length. This product must only be used in hospital environments, clinics or dental offices, by qualified practitioners.

Detect is a modern apex locator intended for precise localization of root canal apex. The measurements in Detect are performed utilizing AC signals at two frequencies - 500 Hz and 8 kHz. The frequencies are alternated and not mixed, eliminating the need for signal mixing and frequency discrimination electronic circuits. The patented signal measuring method utilized in Detect is based on measurements of RMS (Root Mean Square) level of the signal. Advanced user interface implemented in Detect is based on high resolution TFT color graphic display. Clear real time presentation of endodontic file movement inside the canal is designed to make dentist's work easier and to increase his confidence. Display indicators are carefully designed to be intuitively understood and to serve for instant troubleshooting during device usage. Detect shows the movement of the file inside the canal from the beginning of the measurements to the end, providing uninterrupted feedback to the dentist. File tracking algorithm enables full-scale display of the file movement during the treatment while Apical Zoom feature enables high-resolution indication of the file advance in pre-apical and apical zones. Large, clearly recognizable graphical and numerical readings in Apical Zoom are designed to enable precise control over the file advance matching the individual technique of the dentist. Visual information is accompanied by optional audio signals. Numerical values and the numerical scale shown in the Apical Zoom do not represent actual distance from the apex in mm; they serve as a convenient reference to judge the file tip position in relation to the apex. Operation of Detect is fully automatic, no manual calibrations or adjustments are required. The measured signal is analyzed and automatic adjustments are made if required. The device may operate within different conditions in the root canal: dry or wet. Very dry canals should be wetted by hypochlorite or saline solution. Full automation of the apex locator operation simplifies the use and increases the reliability of the measurements. Detect may only be used with stainless steel or nickel titanium endodontic files. Built-in Demo mode of Detect enables easy simulation of all stages of the treatment and is designed to simplify familiarization of the user with the device.

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

The document does not explicitly state numerical acceptance criteria for the "Detect" device. However, it implicitly uses the performance of the predicate device, "BINGO PRO," as the benchmark for substantial equivalence. The key performance aspect for both devices is "precise apex localization" and "clinically acceptable results".

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 explicitly stated, the document only mentions "ex-vivo test was performed on extracted teeth." The exact number of teeth used is not provided.
• Data Provenance: "ex-vivo test was performed on extracted teeth." This indicates the data was collected from biological samples (extracted teeth) outside of a live patient setting. The country of origin is not specified. The study is ex-vivo, which is a form of prospective testing on biological specimens.

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):

This information is not provided in the document. The document simply states that the results of "Detect" were compared to the results of "BINGO PRO," implying that "BINGO PRO" served as a reference or a form of ground truth for the comparison, but without detailing how the true apex location was established for either device or if experts determined this.

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

This information is not provided in the document.

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:

This is not applicable. The device, "Detect," is an electronic apex locator, not an AI system designed to assist human readers (e.g., radiologists interpreting images). The study compares the performance of one device to another, not human performance with and without AI.

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

Yes, a standalone performance test was done. The "Detect" device's performance was evaluated independently during the ex-vivo test and then compared to the "BINGO PRO" device. The document describes the device's automatic operation ("fully automatic, no manual calibrations or adjustments are required") which supports the idea of a standalone assessment of its measurement capabilities.

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

The document implies that the ground truth was established by comparing "Detect" to the results obtained from the "BINGO PRO" apex locator, which is an FDA-cleared device. Therefore, the ground truth is essentially comparison against a predicate device with established performance, rather than an independent gold standard like direct anatomical measurement or pathology results.

8. The sample size for the training set:

This information is not provided in the document. The document describes an ex-vivo test for performance evaluation, but does not detail any internal training sets for the device's development.

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

This information is not provided in the document, as no training set details are given.

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The Detectabuse® Liquid control is intended for use as quality control urine to monitor the precision of laboratory urine toxicology testing procedures for the analytes listed in the package insert.

Not Found

This is a 510(k) premarket notification for a medical device called "Detectabuse® Liquid Control." This document focuses on the regulatory approval of this device, rather than a detailed study report with specific acceptance criteria and performance data in the format requested.

Therefore, the requested information regarding specific acceptance criteria, reported device performance, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth establishment for a clinical study is not present in the provided document.

The document states that the FDA has determined the device is "substantially equivalent" to legally marketed predicate devices. This determination is based on the device meeting the same "indications for use" as existing devices, rather than providing specific performance metrics against pre-defined acceptance criteria in a clinical study context.

In the context of this 510(k) summary, the "acceptance criteria" are broad regulatory requirements for substantial equivalence, and the "study" referred to would be the comparison to predicate devices, which is not detailed in terms of performance metrics.

In summary, the provided document does not contain the specific performance data and study details requested in your prompt.

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The DETECT™ Surgical Pacing and Mapping Tool is a handheld, single use device designed to provide temporary cardiac pacing or monitoring.

The Medtronic® Detect™ Temporary Pacing and Mapping Device Description: Electrode Probe consists of a handle, a malleable stainless steel shaft with a flouropolymer sheath ending in a textured ball tip electrode, and a cable for connection to diagnostic device. Sterile, Nonpyrogenic, Disposable, Single use only.

The Grounding Electrode consists of a needle and a cable for connection to a diagnostic device. Sterile, Nonpyrogenic, Disposable, Single use only.

The Detect™ Electrode Probe is compatible with the Medtronic External Temporary Pacemaker (Model 5388), and the Medtronic Programmer/ Analyzer (Model 2090/2290).

The provided 510(k) summary does not contain detailed acceptance criteria and a study proving the device meets them in the way a modern AI/ML device submission would. This document pertains to a medical device (a temporary pacing and mapping tool) from 2004, which predates the widespread regulatory frameworks for AI/ML-driven devices.

The "study" referenced is a regulatory review for substantial equivalence to a predicate device, rather than a performance study with detailed metrics and statistical significance for a novel AI algorithm.

However, based on the provided text, I can infer and extract information to address your points as best as possible within the context of a 2004 medical device submission.

Here's an analysis of the provided text in response to your questions:

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

Based on the document, the "acceptance criteria" are not explicitly defined as pass/fail thresholds for specific quantitative performance metrics of a novel algorithm. Instead, the acceptance is based on the device being deemed "safe and effective" and "substantially equivalent" to a predicate device, primarily through compliance with established medical device standards and preliminary guidance.

• "Electrode Recording Catheter Preliminary guidance" (FDA, 1995)
• IEC 60601-1
• IEC 60601-27 |
  | Intended Use Fulfilled: Capable of temporary cardiac pacing or monitoring. | The device is designed and demonstrated to provide "temporary cardiac pacing or monitoring." |
  | Compatibility: Works with specified Medtronic devices. | Compatible with Medtronic External Temporary Pacemaker (Model 5388) and Medtronic Programmer/ Analyzer (Model 2090/2290). |

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 describe a "test set" in the context of an AI/ML algorithm's performance evaluation using patient data. It refers to testing of the physical device for safety and effectiveness, which would typically involve bench testing, biocompatibility testing, electrical safety testing, and potentially some limited animal or human clinical data for a novel device, but not a "test set" for an algorithm.

Therefore, this information is not applicable or available in the provided text.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

As there is no mention of a "test set" for an algorithm's performance, there is no discussion of experts establishing ground truth for such a set. The "ground truth" here is the regulatory assessment of device safety and effectiveness and substantial equivalence by regulatory bodies (FDA) based on submitted data and adherence to standards.

Therefore, this information is not applicable or available in the provided text.

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

Similarly, since there's no "test set" for algorithm performance described, there's no adjudication method mentioned.

Therefore, this information is not applicable or available in the provided text.

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

The document does not describe an MRMC comparative effectiveness study or any study involving human readers interacting with an AI system. This is a physical device (an electrode probe), not an AI diagnostic or assistive tool.

Therefore, this information is not applicable or available in the provided text.

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

This refers to an AI algorithm's performance. The device described is a physical medical tool, not an AI algorithm.

Therefore, this information is not applicable or available in the provided text.

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

For this type of traditional medical device, the "ground truth" for regulatory approval would be established through a combination of:

• Bench Testing Data: Verification of electrical performance, mechanical integrity, material compatibility, and sterile barrier integrity against predefined specifications.
• Biocompatibility Testing: Data demonstrating the device's materials do not cause adverse biological reactions.
• Performance Data: Evidence (often from preclinical studies or, if applicable, limited clinical data) that the device performs its intended function (pacing or monitoring) effectively and safely.
• Comparison to Predicate: The predicate device itself serves as a "ground truth" reference for safety and effectiveness, meaning the new device must demonstrate comparable performance characteristics.
  The studies mentioned in the document are compliance with IEC standards, which cover aspects like electrical safety and electromagnetic compatibility.

No human expert consensus, pathology, or specific outcomes data for defining an "algorithm ground truth" is mentioned.

8. The sample size for the training set

The concept of a "training set" is specific to machine learning algorithms. This document describes a physical medical device, not an AI/ML algorithm.

Therefore, this information is not applicable or available in the provided text.

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

As there is no "training set" for an AI/ML algorithm, the method for establishing its ground truth is not applicable.

Therefore, this information is not applicable or available in the provided text.

In summary: The provided 510(k) summary is for a traditional physical medical device from 2004 and does not address the criteria typically associated with AI/ML device submissions (e.g., test sets, training sets, AI performance metrics, expert adjudication for algorithms). The "proof" for this device lies in its substantial equivalence to a legally marketed predicate and its compliance with established safety and performance standards.

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DETECTAR is indicated for use in the detection of subgingival dental calculus.
DETECTAR is indicated for the detection of subgingival dental calculus

The DETECTAR probe is similar in intended use, size, and shape to a manual periodontal probe. The DETECTAR probe contains an optical fiber that reads the optical signature of dental calculus and converts it into an electrical signal. From that electrical signal a computer analysis identifies the dental calculus.

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated in the provided document. However, the study aims to show that DETECTAR performs better than a manual periodontal probe in detecting subgingival dental calculus. The reported device performance is that "The DETECTAR significantly outperformed the manual periodontal probe" in an in vitro evaluation.

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

• Sample Size: Not explicitly stated. The study involved a "piece of pig gingiva... on the root surface of the teeth" and a comparison with a manual periodontal probe. The number of teeth or calculus samples tested is not quantified.
• Data Provenance: In vitro evaluation. The country of origin is not specified but the submitter is from Quebec, Canada. Retrospective or prospective is not applicable for an in vitro study of this nature.

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

• Number of Experts: Three experienced clinicians.
• Qualifications: Described as "experienced clinicians." Specific qualifications (e.g., years of experience, specialty) are not provided beyond "experienced." The document implies these clinicians are performing the evaluations and their observations contribute to the findings.

4. Adjudication Method for the Test Set

The document does not describe an explicit adjudication method. The three experienced clinicians appear to have individually conducted the evaluations and their findings were then compared, leading to the conclusion that DETECTAR "significantly outperformed" the manual probe. It does not mention a consensus-building or tie-breaking process.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described in terms of human readers improving with AI vs. without AI assistance. The study described is a comparison of a device (DETECTAR) against a manual instrument (periodontal probe) in vitro, with clinicians performing the evaluations. It's not a study of human readers' diagnostic accuracy before and after AI assistance.

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was effectively done. The DETECTAR device, which contains an optical fiber and uses "computer analysis" to identify dental calculus, was evaluated on its own in detecting calculus, with the output then presumably interpreted by the clinicians. The "significant outperformance" refers to the device's capability relative to a manual probe.

7. Type of Ground Truth Used

The ground truth used is implicitly the known presence or absence of subgingival dental calculus on the in vitro model (pig gingiva and tooth root). The "drops of blood" were introduced to simulate conditions, suggesting a controlled environment where the presence of calculus could be pre-established or observed reliably by the "experienced clinicians." It is not explicitly stated if a gold standard like histology or micro-CT was used to definitively label the calculus, but rather the clinicians' assessments appear to contribute to the understanding of ground truth or at least the comparative performance.

8. Sample Size for the Training Set

The document does not provide any information about a training set since this appears to be a direct evaluation of the device's performance rather than a validation of a machine learning model that would require a separate training phase. The "computer analysis" identifies dental calculus from an electrical signal, implying a pre-trained algorithm, but the details of that training are not included.

9. How Ground Truth for the Training Set Was Established

Not applicable, as information regarding a training set is not provided in the document.

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for collecting vaginal and cervical cell samples for cytological evaluation and infection diagnosis. The device is not indicated for endometrial cell sampling or for use on pregnant patients.

The Detect™ Cytology Brush II is a one-time use device for collecting vaginal and cervical cell samples for cytological evaluation and infection diagnosis. The device is not indicated for endometrial cell sampling or for use on pregnant patients. The materials of construction have undergone biocompatibility testing and met the requirements of the tests. Testing was also conducted to characterize the performance and integrity of the device. Results were comparable to those obtained when similarly testing a predicate device.

This 510(k) Premarket Notification for the Detect Cytology Brush II is a pre-amendment filing from 1996. It falls under a different regulatory framework than modern medical devices that rely on extensive clinical studies and AI performance metrics.

Therefore, the requested information regarding "acceptance criteria" based on device performance, "study that proves the device meets the acceptance criteria," "sample sizes," "expert ground truth," "adjudication methods," "MRMC studies," "standalone performance," and "training set details" is not applicable to this document.

This document describes a medical device, a cytology brush, that is substantially equivalent to predicate devices already on the market at the time. The 510(k) pathway for this type of device in 1996 primarily focused on:

• Indications for Use: Ensuring the new device is intended for the same purpose as existing devices.
• Technology/Design: Demonstrating that the technology and design are similar to already approved devices.
• Safety (Biocompatibility): Confirming that the materials used are safe for human contact.
• Performance and Integrity (Bench Testing): Showing that the device functions as intended and is robust, often through comparison to a predicate device in controlled lab settings.
• Manufacturing and Quality Assurance: Describing the processes in place to ensure consistent product quality.

Here's a breakdown of what can be extracted from the document in relation to your questions, and why most of your requested fields are not applicable:

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

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

• Not applicable. This document describes bench testing and comparison to a predicate device, not clinical studies with human participants that would involve "test sets" for performance evaluation in the context of diagnostic accuracy. The "testing" mentioned refers to internal engineering and materials testing.

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):

• Not applicable. See point 2. No clinical "test set" requiring expert ground truth in the diagnostic sense was used for this premarket notification.

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

• Not applicable. See point 2.

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:

• Not applicable. This device is a manual cytology brush, a physical instrument. It does not involve AI or human "readers" in the context of image interpretation or diagnostic effectiveness.

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

• Not applicable. This device is a manual instrument, not an algorithm.

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

• Not applicable. The "ground truth" for this device involved engineering specifications and material science for biocompatibility and mechanical performance, and a comparison to an existing predicate device for functional equivalence. It doesn't relate to diagnostic accuracy ground truth.

8. The sample size for the training set:

• Not applicable. This device does not use machine learning or AI, and therefore has no "training set."

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

• Not applicable. This device does not use machine learning or AI, and therefore has no "training set."

In summary: The provided document is a 510(k) summary for a relatively simple, non-AI manual medical device from 1996. The regulatory requirements and the type of evidence presented are fundamentally different from what would be expected for a diagnostic AI device today.

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