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DTX Studio Clinic is a software program for the acquisition, management, transfer and analysis of dental and craniomaxillofacial image information, and can be used to provide design input for dental restorative solutions.

It displays and enhances digital images from various sources to support the diagnostic process and treatment planning. It stores and provides these images within the system or across computer systems at different locations.

It can be used to support guided implant surgery whereby the results can be exported. DTX Studio Clinic is a computer assisted detection (CADe) device that analyses intraoral radiographs to identify and localize dental findings, which include caries, calculus, periapical radiolucency, root canal filling deficiency, discrepancy at the margin of an existing restoration and bone loss. The DTX Studio Clinic CADe functionality is indicated for use by dentists for the concurrent review of bitewing and periapical radiographs of permanent teeth in patients 15 years of age or older.

DTX Studio™ Clinic is a software interface for dental/medical practitioners used to analyze 2D and 3D imaging data, in a timely fashion, for the treatment of dental, craniomaxillofacial and related conditions. DTX Studio Clinic displays and processes imaging data from different devices (i.e. Intra/Extra Oral X-Rays, (CB)CT scanners, intraoral and extraoral cameras).

DTX Studio Clinic features an Al-powered Focus Area Detection algorithm which analyzes 2D intraoral radiographs for potential dental findings or image artifacts. The detected focus areas can be converted afterwards to diagnostic findings after approval by the user.

The following dental findings can be detected by the device:
• Caries
· Discrepancy at marqin of an existing restoration
· Periapical radiolucency

• Root canal filling deficiency
  • Bone loss
  · Calculus

The provided text describes the 510(k) summary for the DTX Studio Clinic (4.0) device, focusing on its substantial equivalence to predicate devices, particularly regarding its AI-powered "Focus Area Detection" functionality. While the document mentions verification and validation activities, it does not provide a detailed breakdown of the acceptance criteria and performance study results as requested by the prompt.

Specifically, it states:

• "A comparative analysis between the output of the focus area detection algorithm of the output of the primary predicate device (K221921) was performed. The test results show that for each intraoral x-ray image, the focus area detection is executed, and the same number of focus areas were detected compared to the predicate device. All detected bounding boxes were identical and the acceptance criteria was met."

This statement indicates that the new device's AI performance for "Focus Area Detection" was evaluated based on its identity to the primary predicate device (K221921), not against independent clinical performance criteria like sensitivity, specificity, or AUC based on a ground truth established by experts. It implies a non-inferiority or equivalence study where the benchmark is the previously cleared AI, rather than a de novo clinical performance evaluation.

Therefore, many of the requested details about the study are not present in this document. I will fill in the table and address the questions based on the information available, highlighting what is not provided.

Acceptance Criteria and Device Performance Study Details for DTX Studio Clinic (4.0) Focus Area Detection

Based on the provided 510(k) summary, the evaluation of the DTX Studio Clinic (4.0)'s AI-powered "Focus Area Detection" functionality was primarily a comparative analysis against its primary predicate device (DTX Studio Clinic 3.0, K221921), rather than a standalone clinical performance study establishing specific diagnostic metrics against a human-expert ground truth. The acceptance criterion appears to be identical output to the predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not explicitly stated. The document refers to "each intraoral x-ray image" without specifying the total number of images used in the comparative analysis.
• Data Provenance: Not explicitly stated (e.g., country of origin). The document implies the use of intraoral x-ray images, but their source, whether retrospective or prospective, or geographical origin, is not detailed.

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

• Not applicable / Not provided. The ground truth for the evaluation appears to be the output of the predicate AI device (K221921), not a human expert consensus. Therefore, no human experts were explicitly stated to establish a direct ground truth for the DTX Studio Clinic (4.0)'s performance itself in this comparative test. The predicate device's original clearance would have been based on such studies.

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

• Not applicable / None specified. Since the comparison was between the subject device's AI output and the predicate AI device's output, there's no mention of human adjudication.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, if so, what was the effect size of how much human readers improve with AI vs without AI assistance

• No. An MRMC comparative effectiveness study was not described for the DTX Studio Clinic (4.0) for this submission. The evaluation was a technical comparison of the AI output to an existing cleared AI, not a study on human-AI augmented performance.

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

• Yes, in spirit, but not as a de novo clinical performance study. The described test was a standalone comparison of the algorithm's output (DTX Studio Clinic 4.0) against another algorithm's output (predicate K221921) to confirm identical functionality. It was not a performance study measuring the algorithm's diagnostic accuracy (e.g., sensitivity, specificity) against a clinical gold standard. The document emphasizes that "There are no functional or technical differences between the Focus Area detection in DTX Studio Clinic 3.0 (primary predicate - K221921) and the current subject device."

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

• The output of the predicate AI device (K221921). The "ground truth" for verifying the DTX Studio Clinic (4.0)'s "Focus Area Detection" functionality was the identical output of the legally marketed predicate device (K221921), which previously underwent its own validation.

8. The sample size for the training set

• Not provided. The document describes the "Focus Area Detection" as an "AI-powered" algorithm using "supervised machine learning," but it does not specify the sample size of the training set used for this algorithm. This information would typically be part of the original K221921 submission.

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

• Not provided. The document states "supervised machine learning" was used, which implies a labeled training dataset. However, the method for establishing the ground truth (e.g., expert annotations, pathology confirmation) for that training data is not detailed in this 510(k) summary. This information would likely have been part of the K221921 submission when the AI algorithm was first cleared.

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DTX Studio Clinic is a computer assisted detection (CADe) device that analyses intraoral radiographs to identify and localize dental findings, which include caries, calculus, periapical radiolucency, root canal filling deficiency, discrepancy at margin of an existing restoration and bone loss.

The DTX Studio Clinic CADe functionality is indicated for the concurrent review of bitewing and periapical radiographs of permanent teeth in patients 15 years of age or older.

DTX Studio Clinic features an AI-powered Focus Area Detection algorithm which analyzes intraoral radiographs for potential dental findings or image artifacts. The detected focus areas can be converted afterwards to diagnostic findings after approval by the user. The following dental findings can be detected by the device: Caries, Discrepancy at margin of an existing restoration, Periapical radiolucency, Root canal filling deficiency, Bone loss, Calculus.

The provided text describes the acceptance criteria and the study that proves the device meets those criteria for the DTX Studio Clinic 3.0.

Here's the breakdown:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" as a pass/fail threshold, but rather presents the performance results from the standalone (algorithm only) and clinical (human-in-the-loop) studies. The acceptance is implicitly based on these results demonstrating clinical benefit and safety.

Standalone Performance (Algorithm-Only)

Clinical Performance (Human-in-the-Loop)

2. Sample Size and Data Provenance

• Test Set (Standalone Performance):

  • Sample Size: 452 adult intraoral radiograph (IOR) images (bitewings and periapical radiographs).
  • Data Provenance: Not explicitly stated, but implicitly retrospective as they were "assembled" and "ground-truthed" for the study.

• Test Set (Clinical Performance Assessment - MRMC Study):

  • Sample Size: 216 periapical and bitewing IOR images.
  • Data Provenance: Acquired in US-based dental offices by either sensor or photostimulable phosphor plates. This suggests retrospective collection from real-world clinical settings in the US.

3. Number of Experts and Qualifications for Ground Truth

• Test Set (Standalone Performance):

  • Number of Experts: A group of 10 dental practitioners followed by an additional expert review.
  • Qualifications: "Dental practitioners" and "expert review" (no further details on experience or specialized qualifications are provided for this set).

• Test Set (Clinical Performance Assessment - MRMC Study):

  • Number of Experts: 4 ground truthers.
  • Qualifications: All ground truthers have "at least 20 years of experience in reading of dental x-rays."

4. Adjudication Method for the Test Set

• Test Set (Standalone Performance): "ground-truthed by a group of 10 dental practitioners followed by an additional expert review." - The specific consensus method (e.g., majority vote) is not explicitly stated.

• Test Set (Clinical Performance Assessment - MRMC Study): Ground truth was defined by 4 ground truthers with a 3 out of 4 consensus. This is an explicit 3+1 (or simply 3 out of 4) adjudication method.

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

• Yes, a MRMC comparative effectiveness study was done.
• Effect Size of Human Readers Improvement with AI vs. Without AI Assistance:
  • The primary endpoint, overall Area Under the Curve (AUC), showed a statistically significant increase of 8.7% (CI [6.5, 10.9], p

Ask a specific question about this device

DTX Studio Clinic is a software program for the acquisition, management, transfer and analysis of dental and craniomaxillofacial image information, and can be used to provide design input for dental restorative solutions. It displays and enhances digital images from various sources to support the diagnostic process and treatment planning. It stores and provides these images within the system or across computer systems at different locations.

DTX Studio Clinic is a software interface for dental/medical practitioners used to analyze 2D and 3D imaging data, in a timely fashion, for the treatment of dental, cramomaxillofacial and related conditions. DTX Studio Clinic displays and processes imaging data from different devices (i.e. intraoral X-Rays, (CB)CT scanners, intraoral scanners, intraoral and extraoral cameras).

This document is a 510(k) Premarket Notification for the DTX Studio Clinic 3.0. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed technical study report with specific acceptance criteria and performance metrics for novel functionalities.

Therefore, the requested information regarding detailed acceptance criteria, specific performance data (e.g., accuracy metrics), sample sizes for test sets, data provenance, expert qualifications, and ground truth establishment for the automatic annotation of mandibular canals is not explicitly detailed in the provided text.

The document states that "Automatic annotation of the mandibular canals" is a new feature in DTX Studio Clinic 3.0, and it is compared to the reference device InVivoDental (K123519) which has "Creation and visualization of the nerve manually or by using the Automatic Nerve feature." However, it does not provide the specific study details for validating this new feature within DTX Studio Clinic 3.0. It only broadly states that "Software verification and validation testing was conducted on the subject device."

Based on the provided text, I cannot fulfill most of the requested information directly because it is not present. The document's purpose is to establish substantial equivalence based on the overall device function and safety, not to detail the rigorous validation of a specific AI/ML component with numerical acceptance criteria.

However, I can extract the available information and highlight what is missing.

Acceptance Criteria and Study for DTX Studio Clinic 3.0's Automatic Mandibular Canal Annotation (Information extracted from the document):

Given the provided text, the specific, quantitative acceptance criteria and detailed study proving the device meets these criteria for the automatic annotation of the mandibular canal are not explicitly described. The document focuses on a broader claim of substantial equivalence and general software validation.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: Not specified for the automatic mandibular canal annotation feature. The document states "Software verification and validation testing was conducted on the subject device," but provides no numbers.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

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

• Adjudication Method: Not specified.

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: Not mentioned or detailed. The document primarily makes a substantial equivalence claim based on the device's overall functionality and features, not a comparative effectiveness study involving human readers.

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

• Standalone Performance: Not explicitly detailed. The document describes the automatic segmentation functionality and mentions that the user can manually adjust, implying a human-in-the-loop scenario. No standalone performance metrics are provided.

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

• Type of Ground Truth: Not specified for the automatic mandibular canal annotation. Given the context of a dental/maxillofacial imaging device, it would likely involve expert annotations on CBCT scans, but this is not confirmed in the text.

8. The sample size for the training set:

• Training Set Sample Size: Not specified. This document is a 510(k) submission, which focuses on validation, not the development or training process.

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

• Ground Truth Establishment for Training Set: Not specified.

Summary of what can be inferred/not inferred from the document regarding the mandibular canal annotation:

• New Feature: Automatic annotation of mandibular canals is a new feature in DTX Studio Clinic 3.0 that was not present in the primary predicate (DTX Studio Clinic 2.0).
• Comparison to Reference Device: This new feature's "functionality is similar as in the reference device InVivoDental (K123519)", which has "Creation and visualization of the nerve manually or by using the Automatic Nerve feature."
• Human Oversight: The user has the ability to "manually indicate or adjust the mandibular canal," suggesting that the automatic annotation is an aid to the diagnostic process, not a definitive, unreviewable output. This is typical for AI/ML features in medical imaging devices that are intended to support, not replace, clinical judgment.
• Validation Claim: The submission states that "Software verification and validation testing was conducted on the subject device and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, 'Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices'." This implies that the validation was performed in accordance with regulatory guidelines, but the specific details of that validation for this particular feature are not disclosed in this public summary.

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DTX Studio Clinic is a software program for the acquisition, management, transfer and analysis of dental and craniomaxillofacial image information, and can be used to provide design input for dental restorative solutions. It displays and enhances digital images from various sources to support the diagnostic process and treatment planning. It stores and provides these images within the system or across computer systems at different locations.

DTX Studio Clinic is a software interface for dental/medical practitioners used to analyze 2D and 3D imaging data, in a timely fashion, for the treatment of dental, craniomaxillofacial and related conditions. DTX Studio Clinic displays and processes imaging data from different devices (i.e. Intraoral and extraoral X-rays, (CB)CT scanners, intraoral scanners, intraoral and extraoral cameras).

Here's a breakdown of the acceptance criteria and study information for the DTX Studio Clinic device, based on the provided text:

Important Note: The provided text is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device, not necessarily a comprehensive clinical study report. Therefore, some information requested (like specific sample sizes for test sets, the number and qualifications of experts for ground truth, adjudication methods, MRMC study effect sizes, and detailed information about training sets) is not explicitly stated in this document. The focus here is on software validation and verification.

Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical "acceptance criteria" in the format of a table with specific metrics (e.g., sensitivity, specificity, accuracy thresholds). Instead, the acceptance is based on demonstrating that the DTX Studio Clinic software performs its intended functions reliably and safely, analogous to the predicate and reference devices, as verified through software validation and engineering testing.

The "reported device performance" is primarily described through the software's functionality and its successful verification and validation.

Study Information

1. Sample sizes used for the test set and the data provenance:

   • Not explicitly stated in the provided text. The document mentions "verification testing" and "validation testing" but does not detail the specific sample sizes of images or patient cases used for these tests.
   • Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective. It focuses on the software's functionality and its comparison to predicate devices, rather than the performance on specific clinical datasets.

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

   • Not explicitly stated in the provided text. The 510(k) summary primarily addresses software functionality verification and validation, not a diagnostic accuracy study involving expert ground truth.

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

   • Not explicitly stated in the provided text.

4. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No, an MRMC comparative effectiveness study was not done or reported. The document explicitly states: "No clinical data was used to support the decision of substantial equivalence." This type of study would involve clinical data and human readers.

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

   • Yes, in spirit. The software validation and verification described are for the algorithm and software functionalities operating independently. While the device does not make autonomous diagnoses (it "supports the diagnostic process and treatment planning"), its individual features (like airway segmentation, image sorting, virtual tooth setup) are tested in a standalone manner in terms of their computational correctness and adherence to specifications. However, this is distinct from standalone clinical performance (e.g., an AI algorithm making a diagnosis without human input). The document focuses on the technical performance of the software.

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

   • For the software verification and validation, the implicit "ground truth" would be the software's functional specifications and requirements. For features like measurements or segmentation, this would likely involve mathematical correctness checks or comparison to pre-defined anatomical models or manually delineated reference segmentations. It is not based on expert consensus, pathology, or outcomes data in a clinical diagnostic sense, as no clinical data was used for substantial equivalence.

7. The sample size for the training set:

   • Not explicitly stated in the provided text. The document describes a medical device software for image management and analysis, not a machine learning model that typically requires a large 'training set' in the deep learning sense. If any features (like the automatic image sorting or virtual tooth setup) utilize machine learning, the details of their training (including sample size) are not provided in this 510(k) summary.

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

   • Not explicitly stated in the provided text. As mentioned above, details about training sets are absent. If machine learning is involved in certain features, the ground truth would typically be established by expert annotation or curated datasets, but this is not detailed here.

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