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Smile Dx® is a computer-assisted detection (CADe) software designed to aid dentists in the review of digital files of bitewing and periapical radiographs of permanent teeth. It is intended to aid in the detection and segmentation of suspected dental findings which include: caries, periapical radiolucencies (PARL), restorations, and dental anatomy.

Smile Dx® is also intended to aid dentists in the measurement (in millimeter and percentage measurements) of mesial and distal bone levels associated with each tooth.

The device is not intended as a replacement for a complete dentist's review or their clinical judgment that takes into account other relevant information from the image, patient history, and actual in vivo clinical assessment.

Smile Dx® supports both digital and phosphor sensors.

Smile Dx® is a computer assisted detection (CADe) device indicated for use by licensed dentists as an aid in their assessment of bitewing and periapical radiographs of secondary dentition in adult patients. Smile Dx® utilizes machine learning to produce annotations for the following findings:

• Caries
• Periapical radiolucencies
• Bone level measurements (mesial and distal)
• Normal anatomy (enamel, dentin, pulp, and bone)
• Restorations

The provided FDA 510(k) Clearance Letter for Smile Dx® outlines the device's acceptance criteria and the studies conducted to prove it meets those criteria.

Acceptance Criteria and Device Performance

The acceptance criteria are implicitly defined by the performance metrics reported in the "Performance Testing" section. The device's performance is reported in terms of various metrics for both standalone and human-in-the-loop (MRMC) evaluations.

Here's a table summarizing the reported device performance against the implied acceptance criteria:

Table 1: Acceptance Criteria and Reported Device Performance

Study Details for Device Performance Proof:

1. Sample Sizes for the Test Set and Data Provenance

• Standalone Testing:
  • Caries and Periapical Radiolucency Detection: 867 cases.
  • Bone Level Detection and Bone Loss Measurement: 352 cases.
  • Normal Anatomy and Restorations: 200 cases.
• MRMC Clinical Evaluation: 352 cases.
• Data Provenance: All test sets were collected from "multiple U.S. sites." The data is retrospective, as it's used in a "retrospective study" for the MRMC evaluation. Sub-group analysis also included "imaging hardware" and "patient demographics (i.e., age, sex, race)," indicating diversity in data.

2. Number of Experts and Qualifications for Ground Truth

• Standalone Testing (Implicit): Not explicitly stated how ground truth for standalone testing was established, but it is likely derived from expert consensus, similar to the MRMC study.
• MRMC Clinical Evaluation: Ground truth was established by the "consensus labels of four US licensed dentists."

3. Adjudication Method for the Test Set

• MRMC Clinical Evaluation: The ground truth for the MRMC study was established by the "consensus labels of four US licensed dentists." This implies a form of consensus adjudication, likely where all four experts reviewed and reached agreement on the findings. The specific method (e.g., majority vote, 2+1, 3+1) is not explicitly detailed beyond "consensus labels." For standalone testing, the adjudication method for ground truth is not specified.

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

• Yes, an MRMC comparative effectiveness study was done.
• Effect Size of Human Readers' Improvement with AI vs. Without AI Assistance:
  • Caries Detection:
    • wAFROC Δθ: +0.127 [0.081, 0.172] (p < 0.001)
    • Sensitivity Improvement: 19.6% increase (from 64.3% without device to 83.9% with device).
    • Specificity Improvement: 16.7% increase (from 73.6% without device to 90.2% with device).
  • Periapical Radiolucency (PARL) Detection:
    • wAFROC Δθ: +0.098 [0.061, 0.135] (p < 0.001)
    • Sensitivity Improvement: 19.1% increase (from 70.7% without device to 89.8% with device).
    • Specificity Improvement: 4.7% increase (from 92.6% without device to 97.3% with device).
• The study design was a "fully-crossed, multiple-reader multiple-case (MRMC) evaluation method" with "at least 13 US licensed dentists (Smile Dx® had 14 readers)." Half of the data set contained unannotated images, and the second half contained radiographs that had been processed through the CADe device. Radiographs were presented to readers in alternating groups throughout two different sessions, separated by a washout period.

5. Standalone Performance Study (Algorithm Only)

• Yes, a standalone performance study was done.
• It evaluated the algorithm's performance for:
  • Caries and Periapical Radiolucency Detection (Dice, Sensitivity)
  • Bone Level Detection and Bone Loss Measurement (Sensitivity, Specificity, Mean Absolute Error)
  • Normal Anatomy and Restorations Detection (Dice, Pixel-level Sensitivity, Contour-level Sensitivity, Contour-level Specificity)

6. Type of Ground Truth Used

• Explicitly for MRMC Study: "Consensus labels of four US licensed dentists." This indicates expert consensus was used for ground truth for the human-in-the-loop evaluation and likely for the standalone evaluation's ground truth as well. There is no mention of pathology or outcomes data.

7. Sample Size for the Training Set

• The document does not explicitly state the sample size for the training set. It only mentions the test set sizes.

8. How Ground Truth for Training Set Was Established

• The document does not explicitly state how ground truth for the training set was established. It mentions the model "utilizes machine learning to produce annotations" and "training data" is used, but provides no details on its annotation process. It's highly probable that expert annotation was also used for the training data, similar to the test set, but this is not confirmed in the provided text.

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The Smileyscope System's Therapy Mode is a prescription-use intual reality system intended to provide adjunctive treatment based on guided relaxation and other evidence-based behavioral methods for patients aged 4-1 years who can cooperate and interact with the device at a developmentally appropriate level. The Smileyscope Therapy Mode is intended to temporarily reduce and/or manage pain and temporarily relieve acute procedural with needle procedures (e.g., venipuncture, IV placement, vaccination, port access, subcutaneous injections). The device is not intended to treat anxiety disorders or specific phobias (e.g. trypanophobia).

Smileyscope is an immersive virtual reality (VR) device, consisting of Hardware and Software components. In Smileyscope Therapy mode, the device delivers 3-dimensional virtual reality treatment based on guided relaxation and other evidence-based behavioral methods to temporarily reduce pain and temporarily relieve acute procedural anxiety in individuals undergoing needle procedures. This prescription-use device uses pre-loaded software on a proprietary hardware and software platform to deliver treatment. The Smileyscope device is supplied with a USB charger and USB cable to facilitate charging.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for the Smileyscope System (Therapy Mode):

Background Note: This document is a 510(k) summary, which often focuses on establishing substantial equivalence to a predicate device rather than presenting a full, detailed clinical study report. Therefore, some specific details about the study methodology (e.g., precise expert qualifications, detailed adjudication methods for ground truth) might not be explicitly stated in this type of summary.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly derived from the "Special Controls" applied to the predicate device, which the Smileyscope Therapy Mode aims to meet, and the "Clinical Performance Testing" results presented.

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

The "test set" here refers to the subjects in the clinical performance studies.

• Sample Size:

  • Emergency Department study: 123 (64 in Smileyscope Therapy Mode arm, 59 in Control arm).
  • Outpatient Pathology study: 129 (63 in Smileyscope Therapy Mode arm, 66 in Control arm).
  • Combined N = 252 (Enrollment N=254, but treatment group totals 252).

• Data Provenance:

  • Country of Origin: "Outside of the United States only". The specific countries are not mentioned in this summary.
  • Retrospective or Prospective: These were "randomized clinical studies" and referenced as "randomized controlled studies," implying a prospective design. Given that they are published in a peer-reviewed journal and were used for regulatory submission, this is a strong indication of prospective data collection.

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

• The summary does not explicitly state the number of experts or their qualifications for establishing ground truth, as the primary effectiveness endpoint was "Child Self-Rated Pain (Faces Pain Scale-Revised)." This is considered a patient-reported outcome (PRO).
• For the secondary endpoint of "procedural anxiety," again, no specific expert involvement for ground truth is mentioned. Procedural anxiety in children is often assessed using validated scales administered by trained researchers or clinicians, or through observational measures.

4. Adjudication Method for the Test Set

• The summary does not explicitly state an adjudication method (e.g., 2+1, 3+1, none) for the test set. Given that the primary endpoint was child self-rated pain, and the studies were randomized controlled trials, the outcome measure itself (Faces Pain Scale-Revised) is directly reported by the patient. Adjudication of such an endpoint by external experts is generally not performed.
• For other aspects of the study (e.g., adverse event reporting, study protocol adherence), there would have been standard clinical trial monitoring and oversight, but this is not detailed as a "ground truth" adjudication.

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

• No, an MRMC comparative effectiveness study was not done in the context of human readers with vs. without AI assistance.
• This device is a virtual reality behavioral therapy device, not an AI-powered diagnostic or assistive tool for human readers. Its primary function is to deliver therapeutic content directly to the patient to reduce pain and anxiety, rather than to assist a human in interpreting data or making a diagnosis. The studies compared the device's therapeutic effect against a control condition.

6. Standalone Performance (Algorithm Only)

• Yes, in essence, standalone performance was done for the "algorithm only" (the therapeutic virtual reality program). The studies measured the direct effect of the Smileyscope Therapy Mode on patients, where patients interacted solely with the device (software content running on specific hardware) and clinicians administered it.
• There was no "human-in-the-loop" component in the sense of a human interpreting AI output or making decisions based on AI assistance. The human (clinician) used the device as a tool, and the "performance" here refers to the device's therapeutic effect on the patient, which was measured directly in the clinical trials against a control.

7. Type of Ground Truth Used

• The primary ground truth used for effectiveness was patient-reported outcomes (PROs), specifically "Child Self-Rated Pain (Faces Pain Scale-Revised)."
• For safety, reported adverse events were the ground truth.

8. Sample Size for the Training Set

• The document does not mention a training set sample size. Because this device delivers virtual reality behavioral therapy, the "algorithm" is the behavioral therapy program itself, not a machine learning model that requires a separate training set in the typical sense.
• The development of the VR experience and its behavioral techniques would have been informed by existing evidence-based behavioral methods, but this is a different concept than a machine learning training set.

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

• As there's no explicit mention of a machine learning training set in the document, establishing ground truth for such a set is not applicable here.
• The "ground truth" for the device's therapeutic approach is implicitly based on "guided relaxation and other evidence-based behavioral methods" which are established practices in psychology and pain management. The clinical studies then validated if the VR delivery of these methods achieved the intended therapeutic effect.

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The SMILE external Trigeminal Nerve Stimulation (eTNS) System is indicated for treatment of pediatric Attention Deficit Hyperactivity Disorder (ADHD) as a monotherapy in patients ages 7 through 12 years old who are not currently taking prescription ADHD medications.

The device is to be used for patient treatment by prescription only and is intended to be used in the home under the supervision of a caregiver during periods of sleep.

Transcutaneous electrical nerve stimulator for Attention Deficit Hyperactivity Disorder. A transcutaneous electrical nerve stimulator for Attention Deficit Hyperactivity Disorder (ADHD) is a prescription device that stimulates transcutaneously or percutaneously through electrodes placed on the forehead.

The SMILE eTNS System treatment protocol is administered each night while the patient is sleeping, for 7-9 hours. The device is designed to provide non-invasive electrical stimulation of the trigeminal nerve.

The provided document is a 510(k) summary for the Nu Eyne Co., Ltd. SMILE device, a transcutaneous electrical nerve stimulator for Attention Deficit Hyperactivity Disorder (ADHD). This document focuses on demonstrating substantial equivalence to a predicate device (Monarch eTNS System), rather than presenting a clinical study where the device performance against specific acceptance criteria is measured for an AI/algorithm-driven device.

Therefore, the requested information regarding acceptance criteria, device performance, sample sizes, expert involvement, adjudication, MRMC studies, standalone performance, and ground truth establishment for an AI/algorithm is not available in this document.

The document details the device's technical specifications, indications for use, and a comparison to a predicate device to establish substantial equivalence. It also lists the non-clinical tests performed (electrical safety, EMC, performance, usability, and software validation according to relevant standards), which support the device's safety and effectiveness in general, but not a specific performance metric against a clinical acceptance criterion in the context of an AI/algorithm.

To directly answer your request based only on the provided text, I must state that the information is not present. This document describes a medical device clearance process focused on equivalence, not on the performance of a machine learning algorithm against a clinical endpoint with a dedicated test set and ground truth.

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The SmileSeries™ is a series of clear, lightweight, plastic appliances indicated for the correction of dental malocclusion in adult and adolescent patients with permanent dentition (i.e. all second molars). The SmileSeries™ positions teeth by way of continuous gentle force.

The SmileSeries™ is comprised of a series of clear, thin, thermoformed removable aligner trays that are designed to correct tooth malocclusions without the use of conventional wire and bracket orthodontic technology. SmileSeries™ aligners are provided non-sterile and are customized for each patient according to the dental clinician's prescription. The dental health professional (dentist/orthodontist) takes provides physical or scanned impressions of the patient's teeth to SmileSeries™. A digital setup of either the scanned impression or a scan of the physical impression is sent to the clinician for approval. Upon approval, molds are then created with 3D-printing technology and the clear aligners are thermoformed on the molds and laser marked. The finished, customized aligners are provided to the dental health care professional who provides them to the patient, confirming fit and design. The aligner trays are held in place by pressure and can be removed by the patients at any time.

This document is a 510(k) Premarket Notification from the FDA for the device "SmileSeries™" by Ordont Orthodontic Laboratories, Inc. The core of this submission is to demonstrate substantial equivalence to a previously cleared predicate device, not to prove that the device meets specific acceptance criteria through a clinical study in the way one might for a novel diagnostic AI algorithm.

Therefore, the requested information regarding "acceptance criteria" and "study that proves the device meets the acceptance criteria" in the context of performance metrics that would be applicable to an AI device (like sensitivity, specificity, MRMC studies, ground truth establishment by experts, etc.) is not present in this document.

Instead, this document describes a comparison to a predicate device based on intended use, technological characteristics, materials, and manufacturing processes, supported by bench testing for manufacturing accuracy and biocompatibility testing of the plastic material.

Here's how to address your points based on the provided document:

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

The document does not present quantitative performance acceptance criteria or reported performance results in the typical sense for a diagnostic device. Since SmileSeries™ is an orthodontic appliance (a physical device), the "acceptance criteria" revolve around demonstrating that it is substantially equivalent to a predicate device, meaning it is as safe and effective as a legally marketed device.

The "performance" is primarily shown through:

• Identical Indications for Use: The SmileSeries™ is used for the same purpose as the predicate.
• Similar Technological Characteristics: Same material (thermoformed plastic), similar manufacturing process (forming plastic sheets on models), and the same software used for planning.
• Successful Bench Testing: To validate the manufacturing process and ensure accuracy of the final aligner compared to the digital scan. No specific numerical results or benchmarks from this bench testing are provided in this summary, only that a "final report was part of the 510(k) package."
• Biocompatibility: The material meets requirements, referencing prior 510(k) submissions.

Table based on the document's comparison of characteristics:

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

This document describes bench testing for manufacturing accuracy, not clinical performance testing with a "test set" of patients or data, as would be common for an AI device. Therefore, a "sample size" in that context is not applicable or provided. The document states:

• "Bench testing was performed to validate the manufacturing process, to ensure the accuracy of the final thermoformed aligner compared to the initial digital scan."
• "A final report was part of the 510(k) package."

No details on the sample size of items tested, data provenance, or retrospective/prospective nature are provided for this bench testing in this summary.

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. This device is an orthodontic appliance, not a diagnostic AI system requiring expert-derived ground truth for a test set. The validation focuses on manufacturing accuracy and material safety, not diagnostic performance.

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

Not applicable. As above, this is not a diagnostic study requiring adjudication of expert readings.

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 is not an AI-assisted diagnostic device. The study performed was bench testing of the physical aligner and its manufacturing process, and biocompatibility testing of the material. The document explicitly states: "In vivo Animal and Human Clinical performance testing are not required for this device category."

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

Not applicable. This is not an algorithm-only device. The reference to software (Ortho Analyzer) is for treatment planning and 3D printing of models for the creation of the aligners, not for standalone diagnostic performance.

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

For the bench testing, the "ground truth" would implicitly be the initial digital scan or design specifications of the aligner, against which the accuracy of the final thermoformed aligner was compared. It's a comparison to a precise digital model, not a biological or clinical ground truth in the sense of disease presence.

8. The sample size for the training set

Not applicable. This refers to an AI training set, which is not relevant to this physical device submission.

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

Not applicable. As per point 8.

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SmileGuard™ light curable resin is indicated for the fabrication of orthodontic and dental appliances such as mouthguards, nightguards and splints.

The SmileGuard™ system combines the light-curable resin, for use with a scanner with design software, validated 3D printer and a curing unit. These components are used together during the additive manufacture of dental appliance splints/mouth guards. The light-curing resin is a proprietary composition of acrylate/methacrylate, methacrylated oligomers and monomers, photo initiators, colorants/dyes and absorbers. It is used by dental laboratories and dental practices to make customized bite splints, using the 3D-printer. The resin is filled in lightproof 1 kg PE bottles labeled and offered together with a programmed RFID chip (referred to as TAG), which is required for use with the validated EnvisionTEC 3D printers. The TAG contains information identifying the resin: material, name and amount. The SmileGuard™ resin is an alternative material to heat-curing and auto-polymerizing resins.

The provided text describes the 510(k) premarket notification for a device named "SmileGuard™", a light-curable resin for fabricating orthodontic and dental appliances. The document is primarily focused on demonstrating substantial equivalence to a predicate device (KeyPrint® KeySplint Soft™) through comparative testing of material properties, rather than an AI-driven medical device requiring clinical performance studies directly involving AI.

Therefore, many of the requested fields regarding acceptance criteria, study design for AI performance, sample sizes for test/training sets, expert adjudication, MRMC studies, and ground truth establishment for AI models are not applicable to this specific device and the information provided. The document focuses on the physical and chemical properties of the resin, its biocompatibility, and manufacturing process.

Here's a breakdown of the provided information relevant to your request, with an explanation of why other aspects are not present:

Device Description:
SmileGuard™ is a light-curable resin used with a scanner, design software, validated 3D printer, and curing unit for the additive manufacture of dental appliance splints/mouth guards.

Intended Use:
Fabrication of orthodontic and dental appliances such as mouthguards, nightguards, and splints.

1. Table of Acceptance Criteria and Reported Device Performance

For this device, the "acceptance criteria" are not performance metrics in the sense of diagnostic accuracy (like sensitivity/specificity for AI), but rather material property specifications and biocompatibility requirements for the resin. The acceptance criteria are based on established ISO and ASTM standards for dental polymers and biocompatibility.

Study Proving Device Meets Acceptance Criteria:

The study proving the device meets the acceptance criteria is a laboratory testing program as outlined in Section VII of the 510(k) Summary.

2. Sample size used for the test set and the data provenance:

• Sample Size: The document does not explicitly state the number of samples (e.g., number of test specimens) used for each physical property test (Tensile, Flexural, Impact, Hardness). It provides mean values and standard deviations, implying multiple samples were tested for each property.
• Data Provenance: The testing was conducted in a laboratory setting. No information about country of origin of data is provided beyond the manufacturer and regulatory consultant being based in Germany and the US respectively. The tests performed are prospective bench tests (physical property measurements and biocompatibility testing on newly manufactured resin samples).

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. This is not a study involving human interpretation of data for "ground truth" (e.g., medical image reading). The "ground truth" for material properties is established by the results of standardized physical and chemical tests performed in a laboratory, and for biocompatibility by the results of in vitro and in vivo biological tests according to ISO standards. No human expert "adjudication" of these test results in the sense of diagnostic interpretation is mentioned or required.

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

• Not applicable. This type of adjudication is relevant for human expert consensus in diagnostic studies (e.g., radiology reads), not for laboratory material testing or biocompatibility assessments.

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 material for fabricating dental appliances, not an AI-driven diagnostic or assistive tool for human interpretation. Therefore, MRMC studies and assessment of human reader improvement with AI assistance are irrelevant.

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

• Not applicable. There is no standalone AI algorithm in the context of this device. The device is a material.

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

• The "ground truth" for this device's performance is derived from objective, quantitative measurements obtained through standardized laboratory tests (e.g., ISO, ASTM standards for material properties) and biological test results for biocompatibility (ISO 10993 series). It is not based on expert consensus, pathology, or outcomes data in the usual clinical sense.

8. The sample size for the training set:

• Not applicable. This is not an AI model requiring a training set.

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

• Not applicable. No training set for an AI model.

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SMILE-100 System is intended to review, measure and record skin temperature patterns and variations emitted from the human body. It is intended for use as adjunctive diagnostic imaging for themally significant indications in the breast region. The significance of the value of these thermal patterns is determined by profession. This device is intended for use by qualified healthcare personnel trained in its use. The system is not intended for absolute temperature measurements. The system is not intended to be used as a thermometry device.

The SMILE-100 System is a breast thermography device with a visualization tool that helps a healthcare personnel to review, measure and analyze thermally significant indications in the breast region. It is intended to be used in the hospital, acute care settings, outpatient surgery, healthcare practitioner facilities or an environment where patient care is provided by qualified healthcare personnel.

SMILE-100 System consists of the following:

• An off-the-shelf FDA cleared thermal camera with its associated camera control software (i) provided by the thermal camera vendor for capturing and viewing thermal images
• An off the shelf laptop/desktop computer system with display, keyboard and mouse. (ii)
• SMILE-100 Software for viewing thermal patterns in thermal images (iii)

The SMILE-100 Software takes thermal images captured using off-the-shelf FDA-cleared thermal camera and provides various visualization options in multiple customizable views/palettes and also generates a report with quantitative thermal parameters and annotated images. The Software supports two user roles (i) a thermographer or imaging technician role and (ii) Expert thermologist role. The imaging technician captures thermal images and uploads them to the cloud-hosted SMILE-100 Software which performs image quality checks and submits to Expert. The Expert can select a temperature threshold for the SMILE-100 Software to highlight areas with thermal activity above the threshold in thermal images. The software makes no estimation of the thermal threshold. The software makes no determination regarding what the thermal patterns or relative temperature values mean. Expert thermologist needs to infer the meaning of high thermal activity and other areas of interest based on his/her visual interpretation of those patterns and thermal values.

Here's a breakdown of the acceptance criteria and study information for the SMILE-100 System, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state "acceptance criteria" in a quantitative manner for the SMILE-100 software's diagnostic performance or a clinical study proving its effectiveness in identifying thermally significant indications. The performance testing section primarily focuses on technical verification and comparison with a predicate device's technological characteristics.

However, based on the provided comparison tables, we can infer some technical performance characteristics that are critical for equivalence with the predicate device.

Note: The document explicitly states: "The software makes no determination regarding what the thermal patterns or relative temperature values mean. Expert thermologist needs to infer the meaning of high thermal activity and other areas of interest based on his/her visual interpretation of those patterns and thermal values." This indicates that the device's technical performance is about accurate capture and display of thermal data, and the diagnostic interpretation remains with the human expert. Therefore, a clinical performance study with sensitivity/specificity metrics for disease detection is not presented for the software itself.

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

The provided document does not detail a specific test set sample size or data provenance (country of origin, retrospective/prospective) for a clinical performance study of the SMILE-100 software's diagnostic capabilities.

The "Performance Testing" section mentions:

• "Verification during Product Development"
• "Verification of SMILE-100 System Output Parameters"
• "Interoperability Testing with different Thermal Camera Models"
• "Comparison of the equivalence of temperature values between thermal camera and SMILE-100 Software"

These are technical verification activities, not a clinical performance study involving patient data for diagnostic accuracy.

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

Not applicable. As there is no clinical performance study on a test set mentioned in the document, there is no information about experts used for establishing ground truth. The device relies on the interpretation of an "Expert thermologist."

4. Adjudication Method for the Test Set

Not applicable. No clinical test set or adjudication method is described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No. The document does not describe an MRMC comparative effectiveness study involving human readers with and without AI assistance. The SMILE-100 software provides visualization and quantitative parameters, but the "Expert thermologist" is still responsible for interpretation.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

No. The document explicitly states: "The software makes no determination regarding what the thermal patterns or relative temperature values mean... Expert thermologist needs to infer the meaning of high thermal activity and other areas of interest based on his/her visual interpretation of those patterns and thermal values." This confirms that the device is not intended for standalone diagnostic performance. It is an "adjunctive diagnostic imaging" tool.

7. The Type of Ground Truth Used

Not applicable. Since no clinical performance study evaluating diagnostic accuracy is presented, there is no ground truth type (pathology, outcomes data, expert consensus, etc.) mentioned in relation to the SMILE-100 software's performance as a diagnostic aid.

8. The Sample Size for the Training Set

Not mentioned. The document does not provide details on a training set size, as it focuses on technical verification and equivalence rather than a machine learning model's diagnostic performance for which training data would be required.

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

Not applicable/Not mentioned. Similar to point 8, with no training set data or a clinical performance study described, the method for establishing ground truth for a training set is not provided.

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Biotech Dental SMILERS® aligners are indicated for the alignment of teeth in patients with permanent dentition (i.e., all second molars) during orthodontic treatment of malocclusion. SMILERS® aligners position teeth by way of continuous gentle force.

The Biotech Dental SMILERS® aligners are a series of prescription-only clear plastic removable aligners intended to incrementally move a patient's teeth from an initial position to a different end position using a software-generated sequence of intermediate states. Biotech Dental SMILERS® sequentially reposition teeth by way of continuous gentle force.

The provided text is an FDA 510(k) summary for the Biotech Dental SMILERS® aligners. It focuses on demonstrating substantial equivalence to a predicate device (Byte Aligner System K180346) rather than detailing acceptance criteria and a study that proves the device meets them in the context of an AI/algorithm-driven device.

However, I can extract the information relevant to non-clinical performance testing and conceptualize how it relates to acceptance criteria and "proving the device meets them," even if it’s not an AI performance study.

Key takeaway from the document: This submission is for orthodontic aligners, which are physical medical devices, not an AI/algorithm. The "acceptance criteria" and "study that proves the device meets the acceptance criteria" in this context refer to manufacturing validation, material testing, and shelf-life studies, not clinical performance or AI algorithm performance. The FDA determined that no clinical data was needed due to the well-established nature of sequential aligners.

Therefore, the requested information points 1 through 9, which are largely geared towards AI/algorithm performance studies, are not explicitly present in the provided document in the way they would be for an AI-medical device. I will address them based on the information available and note where it's not applicable.

Acceptance Criteria and Device Performance (Based on Non-Clinical Testing for a Physical Device)

1. Table of Acceptance Criteria and Reported Device Performance

Regarding AI/Algorithm-Specific Questions (Not Applicable to this Device Approval, but addressed for completeness):

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

• Not Applicable (N/A): This submission is for a physical medical device (orthodontic aligners), not an AI algorithm. No test set of clinical data (images, etc.) for AI performance was used. The "test set" for this application refers to physical samples used in manufacturing validation and biocompatibility testing. The number of cases for manufacturing validation is not specified, nor is the provenance of those cases.

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):

• N/A: As this is physical device approval, there's no clinical "ground truth" established by experts in the context of an AI algorithm's diagnostic performance. For the fit validation, it states "qualitatively evaluated by a qualified individual," but specific number or detailed qualifications beyond "qualified" are not provided.

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

• N/A: No clinical data or AI performance test set requiring adjudication in this context.

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:

• N/A: This is not an AI-assisted device. No MRMC study was performed. The clinical performance of sequential aligners is stated to be "well established" and "no clinical data is included in this submission."

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

• N/A: This is not an algorithm. The Nemocast software (K193003) is mentioned as a tool for planning cases, but the approval is for the aligners themselves, not the software's performance as a standalone diagnostic or treatment algorithm.

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

• N/A (for AI context): For the physical device, "ground truth" for manufacturing and fit would be based on engineering specifications and qualitative assessment of fit by a "qualified individual." Biocompatibility is assessed against published international standards.

8. The sample size for the training set:

• N/A: There is no AI training set for this device approval.

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

• N/A: There is no AI training set for this device approval.

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The SmileAlign® Orthodontic Aligner System is indicated for the treatment of tooth malocclusion in patients with permanent dentition (i.e., all second molars). The SmileAlign Orthodontic Aligner System positions teeth by way of continuous gentle force.

The SmileAlign® Orthodontic Aligner System is a series of dental aligners fabricated of clear, thin thermoformed polyethylene terephthalate glycol (PETG) plastic to progressively reposition the teeth. Corrective force to straighten the teeth is delivered via minor changes into a modified position in each subsequent aligner.

This document describes the 510(k) premarket notification for the SmileAlign® Orthodontic Aligner System (K191774). However, it does not contain any information about an AI/Algorithm-driven device that would require an evaluation against specific acceptance criteria for algorithm performance, a test set, expert ground truth establishment, or clinical performance data related to AI assistance.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (Byte Aligner System, K180346) based on material properties, indications for use, technological characteristics, and biocompatibility. It explicitly states: "Clinical data was not included in this submission."

Therefore, based on the provided text, I cannot fill in the requested table and information points regarding acceptance criteria and study data for an AI-driven device's performance, as this information is not present in the given FDA 510(k) summary.

The document describes the device as: "a series of dental aligners fabricated of clear, thin thermoformed polyethylene terephthalate glycol (PETG) plastic to progressively reposition the teeth. Corrective force to straighten the teeth is delivered via minor changes into a modified position in each subsequent aligner." The only mention of software is that "Both the subject and predicate devices use independent dental software to translate tooth movements in developing the model schemes and allow the dental practitioner to review and approve the model schemes before aligner fabrication." This refers to standard CAD/CAM software used in the dental industry, not an AI algorithm performing diagnostic or treatment planning functions.

If this was a request for an AI-driven device, the provided text does not contain the necessary information.

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Smile Makers Personal Lubricants are water-based personal lubricants for vaginal application, intended to lubricate and moisturize, to enhance the ease and comfort of intimate sexual activity and supplement the body's natural lubrication. This product is compatible with natural rubber latex and polyisoprene condoms. This product is not compatible with polyurethane condoms.

Smile Makers personal lubricants are water-based personal lubricants. There are three formulations, Generous Gel, Stay Silky Serum, and Little Light Liquid, that contain the same ingredients, and only differ in amounts of specific ingredients. Ingredients of the lubricants include the following: water (aqua), glycerin, propylene glycol, methyl propanediol, carbomer, sodium benzoate, sodium hydroxide, and dipotassium glycyprizate. They are compatible with natural rubber latex and polyisoprene condoms, but are not compatible with polyurethane condoms. They are provided non-sterile. The product is packaged in 30 ml transparent plastic bottles made of acrylonitrile styrene and polyethylene terephthalate. The bottle contains a pump dispenser made of polypropylene and the cap is made of an aluminum coated low density polyethylene. It is for over-the-counter use.

This document describes the premarket notification (510(k)) for "Smile Makers Personal Lubricants." Therefore, the information provided focuses on demonstrating substantial equivalence to a predicate device, rather than a study proving the device meets acceptance criteria in the context of an AI-powered medical device study. Concepts like "acceptance criteria," "sample size for test set," "number of experts," "adjudication method," "MRMC study," "standalone performance," "ground truth," and "training set" are specific to the development and validation of AI/ML medical devices, which is not what this document addresses.

However, I can extract the closest analogous information from the provided text regarding the device specifications and the studies performed to demonstrate performance and safety in relation to these specifications and the predicate device.

Here's an interpretation based on the provided document, restructured to address your questions where possible, and noting where the information is not applicable to a traditional AI/ML device study:

Device: Smile Makers Personal Lubricants (Smile Makers Little Light Liquid, Smile Makers Stay Silky Serum, and Smile Makers Generous Gel)

Purpose of the Submission: 510(k) Premarket Notification to demonstrate substantial equivalence to a legally marketed predicate device (BioFilm, Inc. - Astroglide Sensual Strawberry, K140590).

1. Table of Acceptance Criteria (Specifications) and Reported Device Performance

The acceptance criteria here refer to the product specifications that the manufacturer sets for the lubricant. The "reported device performance" refers to the results of tests demonstrating compliance with these specifications.

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

This is not applicable as this is not an AI/ML device study. The "test set" here would refer to the specific samples of lubricant used for the various physical, chemical, and biological tests. The document does not specify the exact batch sizes or numbers of samples tested for each parameter (e.g., how many bottles were tested for viscosity, how many animal subjects for biocompatibility). The studies are conducted by the manufacturer, LFBeauty (Thailand) Limited. The data provenance is from in vitro laboratory tests and in vivo animal tests, performed by or on behalf of the manufacturer, to demonstrate product specifications and safety/performance.

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

Not applicable. This is not an AI/ML device requiring human expert annotation for ground truth. Technical specifications are verified through standardized laboratory test methods (e.g., USP, ISO, ASTM).

4. Adjudication Method for the Test Set

Not applicable. Ground truth for physical/chemical/biological properties is established by defined test methods, not by human adjudication of opinions.

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 is not an AI-powered medical device.

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

Not applicable. This is not an AI-powered medical device.

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

The "ground truth" for the device's characteristics and performance is established through standardized laboratory testing protocols (USP, ISO, ASTM) for chemical, physical, and biological properties, and in vivo animal model biocompatibility studies. For example, pH is measured by a pH meter against calibrated standards, microbial counts are determined by microbiological culture methods, and condom compatibility is assessed by stress-strain tests per ASTM standards.

8. The sample size for the training set

Not applicable. This is not an AI/ML device.

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

Not applicable. This is not an AI/ML device.

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SmileBond Systems Fast Bite Registration material is used for making impressions of the tech and gums of patients requiring enhancement of the smile and for bite alignment.

SmileBond System is a patented process for the delivery of dental bonding composites. The SmileBond process is indicated for both cosmetic enhancement of the smile, and functional improvement of the bite (or chewing system).

Fast-Set Bite Registration is a silicone impression material intended to be placed on an impression tray to reproduce the structures of the teeth and gums of patients requiring the enhancement of the smile and for bite alignment. The impression material is intended to be applied via a dual barrel cartridge system into an impression tray. The clear impression material is used in the lab to make an accurate impression of the final wax up. Once the impression material is set and slightly modified it is taken to the mouth for use in the transfer bonding process.

This 510(k) submission (K132599) concerns the "SmileBond System: Fast-Set Bite Registration Material," which is a dental impression material. The submission establishes substantial equivalence to existing predicate devices, rather than introducing a novel AI-powered diagnostic device.

Therefore, many of the requested categories related to AI performance, such as multi-reader multi-case studies, standalone algorithm performance, training/test set details, and expert adjudication of AI results, are not applicable to this type of device.

Here's the information that can be extracted and inferred from the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

For dental impression materials, acceptance criteria typically revolve around physical and mechanical properties, as well as biological compatibility. These are usually evaluated through laboratory testing following relevant ISO standards. However, the provided summary does not detail specific acceptance criteria or performance metrics (e.g., accuracy, tear strength, dimensional stability, working time, setting time).

Instead, the submission focuses on demonstrating substantial equivalence based on:

• Intended Use: Making impressions of teeth and gums for smile enhancement and bite alignment.
• Technological Characteristics: Silicone impression material, dual-barrel cartridge delivery, used with an impression tray.
• Comparison to Predicate Devices: Stating that the material is comparable to previously cleared silicone impression materials.

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

Since this is a dental impression material, the "test set" would typically refer to samples of the material used in laboratory or potentially in-vivo/clinical performance studies (e.g., for physical properties, biocompatibility).

• The provided document does not specify sample sizes for any performance testing.
• Data Provenance: The document does not provide details on the origin of any performance data. The device is manufactured by Crown Delta under a previous 510(k) (K801380) for Millennium Company, suggesting that performance data, if generated, might refer to that previous clearance.

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

• This is not applicable to a dental impression material. "Ground truth" in this context would refer to objective measurements of the material's properties (e.g., dimensional accuracy, tear strength) obtained through standardized laboratory tests, not expert consensus on diagnostic images.

4. Adjudication Method for the Test Set

• This is not applicable to a dental impression material. Adjudication is relevant for diagnostic interpretations, not for material property assessments.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

• This is not applicable as this is not an AI/diagnostic device that improves human reader performance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• This is not applicable as this is not an AI/algorithm-based device.

7. The Type of Ground Truth Used

• For an impression material, the "ground truth" would be objective measurements of its physical and mechanical properties (e.g., working time, setting time, dimensional stability, elasticity, tear strength) typically established against ISO standards (e.g., ISO 4823 for elastomeric impression materials). The provided document does not explicitly state which specific ground truth measurements were used, but it implicitly relies on the material meeting the standards established for its predicate devices by Crown Delta.

8. The Sample Size for the Training Set

• This is not applicable as this is not an AI/machine learning device that requires a training set.

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

• This is not applicable as this is not an AI/machine learning device.

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