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DTX Studio Assist is a Software Development Kit (SDK) designed to integrate with medical device software that displays two-dimensional dental radiographs. It contains a selection of algorithms that processes input data (two-dimensional radiographs) from the hosting application and returns a corresponding output to it.

DTX Studio Assist is intended to support the measurement of alveolar bone levels associated with each tooth. It is also intended to aid in the detection and segmentation of non-pathological structures (i.e., restorations and dental anatomy).

DTX Studio Assist contains a computer-assisted detection (CADe) function that analyzes bitewing and periapical radiographs of permanent teeth in patients aged 15 and older to identify and localize dental findings, including caries, calculus, periapical radiolucency, root canal filling deficiency, discrepancy at the margin of an existing restoration, and bone loss.

DTX Studio Assist is not intended as a replacement for a complete dentist's review nor their clinical judgment which takes into account other relevant information from the image, patient history, and actual in vivo clinical assessment.

DTX Studio Assist is a software development kit (SDK) that makes a selection of algorithms (including AI-based algorithms) available through a clean, well-documented API. DTX Studio Assist features are only available to licensed customers. The SDK has no user interface and is intended to be bundled with and used through other software products (hosting applications).

Key functionalities of DTX Studio Assist include:

Focus Area Detection on IOR images: The software features the Focus Area Detection algorithm which analyzes intraoral radiographs for potential dental findings (caries, periapical radiolucency, root canal filling deficiency, discrepancy at the margin of an existing restoration, bone loss and calculus) or image artifacts.

Alveolar Bone Level Measurement: The software enables the measurements of mesial and distal alveolar bone levels associated with each tooth.

Detection of historical treatments: The software enables automated detection and segmentation of dental restorations in IOR images to support dental charting which can be used during patient communication. The following restoration types are supported: amalgam fillings, composite fillings, prosthetic crowns, bridges, implants, implant abutments, root canal fillings and posts.

Anatomy Segmentation: The software segments dental structures by assigning a unique label to each pixel in IOR images, including enamel, dentine, pulp, bone, and artificial structures.

Here's a breakdown of the acceptance criteria and the studies that prove the device meets them, based on the provided FDA 510(k) Clearance Letter.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document does not explicitly state pre-defined acceptance criteria for the new features (Restoration Detection, ABL Measurement, Anatomy Segmentation). Instead, it presents the achieved performance metrics, implying that these values were considered acceptable. For the CADe function, the acceptance criteria are implied by the statistically significant improvement observed in the MRMC study.

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

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

4. Adjudication Method for the Test Set Ground Truth

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

Yes, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done for the Focus Area Detection (CADe) functionality.

Effect Size: The study demonstrated a highly significant AUC increase (p < 0.001) of 8.7% overall in the aided arm (human readers with AI assistance) compared to the unaided control arm (human readers without AI assistance). This indicates that the AI significantly improved dentists' diagnostic detection and localization performance.

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

Yes, standalone performance studies were done for all functionalities mentioned:

• Focus Area Detection (CADe): While the primary demonstration of effectiveness was through an MRMC study, the summary states, "The standalone performance testing results supporting this feature are included in that submission [K221921]," indicating standalone testing was performed.
• Restoration Detection Algorithm: "A standalone performance assessment was conducted to evaluate the DTX Studio Assist IOR Restoration Detection algorithm independently, without interaction from dental professionals..."
• Alveolar Bone Level (ABL) Measurement Algorithm: "A standalone performance assessment was conducted to evaluate the DTX Studio Assist IOR Alveolar Bone Level (ABL) Measurement algorithm independently, without interaction from dental professionals..."
• Anatomy Segmentation Algorithm: "A standalone performance assessment was conducted to evaluate the DTX Studio Assist IOR Anatomy Segmentation algorithm independently, without interaction from dental professionals..."

7. The Type of Ground Truth Used

8. The Sample Size for the Training Set

The document does not provide the specific sample size for the training set for any of the algorithms. It focuses on the validation (test) sets.

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

The document does not provide details on how the ground truth for the training set was established. It only describes the ground truth establishment for the test sets. It does mention that the algorithms are based on "supervised machine learning algorithms," which inherently means they were trained on data with established ground truth.

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Second Opinion® Panoramic is a radiological automated image processing software device intended to identify and mark regions, in panoramic radiographs, in relation to suspected dental findings which include: Caries, Periapical radiolucency, and Impacted third molars.

It is designed to aid dental health professionals to review panoramic radiographs of permanent teeth in patients 16 years of age or older as both a concurrent and second reader.

Second Opinion® PR is a radiological automated image processing software device intended to identify and mark regions, in panoramic radiographs, in relation to suspected dental findings which include: caries, periapical radiolucency, and impacted third molars. It should not be used in lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis.

It is designed to aid dental health professionals to review panoramic radiographs of permanent teeth in patients 16 years of age or older as a concurrent and second reader.

Second Opinion® PR consists of three parts:

• Application Programing Interface ("API")
• Machine Learning Modules ("ML Modules")
• Client User Interface (UI) ("Client")

The processing sequence for an image is as follows:

1. Images are sent for processing via the API
2. The API routes images to the ML modules
3. The ML modules produce detection output
4. The UI renders the detection output

The API serves as a conduit for passing imagery and metadata between the user interface and the machine learning modules. The API sends imagery to the machine learning modules for processing and subsequently receives metadata generated by the machine learning modules which is passed to the interface for rendering.

Second Opinion® PR uses machine learning to detect regions of interest. Images received by the ML modules are processed yielding detections which are represented as metadata. The final output is made accessible to the API for the purpose of sending to the UI for visualization. Detected regions of interest are displayed as mask overlays atop the original radiograph which indicate to the practitioner which regions contain which detected potential conditions that may require clinical review. The clinician can toggle over the image to highlight a potential condition for viewing.

Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) Clearance Letter:

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

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

• Sample Size (Test Set): An "enriched regionally balanced image set of 795 images" was used for the clinical evaluation.
• Data Provenance:
  • Country of Origin: Not explicitly stated for each image, but geographically diverse, described "with respect to the United States" and including specific regions (Northwest, Northeast, South, West, Midwest).
  • Retrospective/Prospective: The study is described as "retrospective" due to "non-patient-contact nature."

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

• Number of Experts: Four board-certified dentists.
• Qualifications of Experts: Each possessed "a minimum of five years practice experience" as ground truth readers.

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

• Adjudication Method: Consensus approach based on agreement among at least three out of four expert readers. (This is a 3-out-of-4 or 3/4 consensus method).

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

• Yes, a fully-crossed MRMC study was done.
• Effect Size of Improvement (AI-aided vs. unaided reading):
  • Periapical Radiolucency:
    • Lesion level wAFROC difference: 0.0705 (95% CI: 0.04–0.10)
    • Image level wAFROC difference: 0.0715 (95% CI: 0.07–0.07)
    • Lesion-level sensitivity gain: 0.2045 (95% CI: 0.17–0.24)
  • Caries:
    • Lesion level wAFROC difference: 0.0306 (95% CI: 0.00–0.06)
    • Image level wAFROC difference: 0.0176 (95% CI: 0.02–0.02)
    • Lesion-level sensitivity gain: 0.1169 (95% CI: 0.08–0.15)
  • Impacted Teeth:
    • Lesion level wAFROC difference: 0.0093 (95% CI: 0.00–0.02)
    • Image level wAFROC difference: 0.0715 (95% CI: 0.07–0.07)
    • Lesion-level sensitivity gain: 0.0192 (95% CI: 0.01–0.03)

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

• Yes, a standalone clinical study was done. The results are discussed in the "Standalone Testing" section, demonstrating the algorithm's performance independent of human readers.

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

• Type of Ground Truth: Expert consensus. Specifically, "consensus ground truth established by expert dental radiologists" using agreement among the four board-certified dentists.

8. The sample size for the training set

• The document does not provide the sample size for the training set. It only describes the test set size.

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

• The document does not specify how the ground truth for the training set was established. It only details the ground truth establishment process for the test set.

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SugarBug is a radiological, automated, concurrent read, computer-assisted detection software intended to aid in the detection and segmentation of caries on bitewing radiographs. The device provides additional information for the dentist to use in their diagnosis of a tooth surface suspected of being carious. Sugarbug is intended to be used on patients 18 years and older. 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.

SugarBug is a software as a medical device (SaMD) that uses machine learning to label features that the reader should examine for evidence of decay. SugarBug uses convolutional neural network to perform a semantic segmentation task. The algorithm goes through every pixel in an image and assigns a probability value to it for the possibility that it contains decay. A threshold is used to determine which pixels are labeled in the device's output. The software reads the selected image using local processing; images are not imported or sent to a cloud server any time during routine use.

Here's a breakdown of the acceptance criteria and the study details for the SugarBug (1.x) device, based on the provided FDA 510(k) clearance letter:

1. Acceptance Criteria and Reported Device Performance

The direct "acceptance criteria" are not explicitly stated in a quantitative table for this device. However, based on the clinical study results and the stated objectives, the implicit acceptance criteria would have been:

• Statistically significant improvement in overall diagnostic performance (wAFROC-AUC) for aided readers compared to unaided readers.
• Demonstrated improvement in lesion-level sensitivity for aided readers.
• Maintain or improve lesion annotation quality (DICE scores) with aid.
• Standalone performance metrics (sensitivity, FPPI, DICE coefficient) within an acceptable range.

Here's a table summarizing the reported device performance against these implicit criteria:

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

• Sample Size for Test Set (MRMC Study): 300 bitewing radiographic images.
• Sample Size for Test Set (Standalone Study): 400 de-identified images.
• Data Provenance: Retrospectively collected from routine dental examinations of patients aged 18 and older from the US. The images were sampled to be representative of a range of x-ray sensor types (Vatech HD 29%, iSensor H2 11%, Schick 33: 45%, Dexis Platinum 15%).

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

• Number of Experts: 3 US licensed general dentists.
• Qualifications: Mean of 27 years of clinical experience.

4. Adjudication Method for the Test Set

• Adjudication Method (Ground Truth): Consensus labels of the 3 US licensed general dentists.

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

• Was a MRMC study done? Yes.
• Effect size of human readers improvement with AI vs. without AI assistance:
  • wAFROC-AUC Improvement: 0.066 (0.030, 0.102) with a p-value of 0.001.
  • Lesion-Level Sensitivity Improvement: 0.134 (0.066, 0.206).

6. Standalone (Algorithm Only) Performance Study

• Was a standalone study done? Yes.
• Performance metrics:
  • Lesion-level sensitivity: 0.686 (0.655, 0.717)
  • Mean FPPI: 0.231 (0.111, 0.303)
  • DICE coefficient versus ground truth: 0.746 (0.724, 0.768)

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus (established by 3 US licensed general dentists).

8. Sample Size for the Training Set

• The document does not explicitly state the sample size used for the training set. It only describes the test sets.

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

• The document does not explicitly state how the ground truth for the training set was established. It only mentions that the standalone testing data (which could be considered a "test set" for the standalone algorithm) was "collected and labeled in the same procedure as the MRMC study," implying expert consensus was used for that, but it doesn't specify for the training data.

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Videa Dental AI is a computer-assisted detection (CADe) device that analyzes intraoral radiographs to identify and localize the following features. Videa Dental AI is indicated for the review of bitewing, periapical, and panoramic radiographs acquired from patients aged 3 years or older.

Suspected Dental Findings:

• Caries
• Attrition
• Broken/Chipped Tooth
• Restorative Imperfection
• Pulp Stones
• Dens Invaginatus
• Periapical Radiolucency
• Widened Periodontal Ligament
• Furcation
• Calculus

Historical Treatments:

• Crown
• Filling
• Bridge
• Post and Core
• Root Canal
• Endosteal Implant
• Implant Abutment
• Bonded Orthodontic Retainer
• Braces

Normal Anatomy:

• Maxillary Sinus
• Maxillary Tuberosity
• Mental Foramen
• Mandibular Canal
• Inferior Border of the Mandible
• Mandibular Tori
• Mandibular Condyle
• Developing Tooth
• Erupting Teeth
• Non-matured Erupted Teeth
• Exfoliating Teeth
• Impacted Teeth
• Crowding Teeth

Videa Dental AI (VDA) software is a cloud-based AI-powered medical device for the automatic detection of the features listed in the Indications For Use statement in dental radiographs. The device itself is available as a service via an API (Application Programming Interface) behind a firewalled network. Provided proper authentication and an eligible bitewing, periapical or panoramic image, the device returns a set of bounding boxes and/or segmentation outlines depending on the indication representing the suspect dental finding, historical treatment or normal anatomy detected.

VDA is accessed by the dental practitioner through their dental image viewer. From within the dental viewer the user can upload a radiograph to VDA and then review the results. The device outputs a binary indication to identify the presence or absence of findings for each indication. If findings are present the device outputs the number of findings by finding type and the coordinates of the bounding boxes/segmentation outlines for each finding. If no findings are present the device outputs a clear indication that there are no findings identified for each indication. The device output will show all findings from one radiograph regardless of the number of teeth present.

The intended users of Videa Dental AI are trained dental professionals such as dentists and dental hygienists. For the suspect dental findings indications specifically, VDA is intended to be used as an adjunct tool and should not replace a dentist's review of the image. Only dentists that are performing diagnostic activities shall use the suspect dental finding indications.

VDA should not be used in-lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. The system is to be used by trained dental professionals including, but not limited to, dentists and dental hygienists.

Depending on the specific VDA indication for use, the intended patients of Videa Dental AI are patients 3 years of age and older above with primary, mixed and/or permanent dentition undergoing routine dental visits or suspected of one of the suspected dental findings listed in the VDA indications for use statement above. VDA may be used on eligible bitewing, periapical or panoramic radiographs depending on the indication.

See Table 1 below for the specific patient age group and image modality that each VDA indication for use is designed and tested to meet. VDA uses the images metadata to only show the indications for the patient age and image modalities in scope as shown in Table 1. VDA will not show any findings output for an indication for use that is outside of the patient age and radiographic view scope.

Here's a summary of the acceptance criteria and study details for Videa Dental AI, based on the provided FDA 510(k) Clearance Letter:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state numeric acceptance criteria thresholds for all indications. However, it implicitly states that Videa Dental AI meets its performance requirements by demonstrating statistically significant improvement in detection performance for clinicians when aided by the device compared to unaided performance in the clinical study for certain indications. For standalone performance, DICE scores are provided for caries, calculus, and normal tooth anatomy segmentations.

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

• Standalone Performance Test Set:
  • Sample Size: 1,445 radiographs
  • Data Provenance: Collected from more than 35 US sites (retrospective, implied, as it's for ground-truthing/benchmarking).
• Clinical Performance (MRMC) Test Set:
  • Sample Size: 378 radiographs
  • Data Provenance: Collected from over 25 US locations spread across the country (retrospective, implied, as it's for ground-truthing/benchmarking).

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

• Standalone Performance Test Set:
  • Number of Experts: Three
  • Qualifications: US board-certified dentists.
• Clinical Performance (MRMC) Test Set:
  • Number of Experts: Not explicitly stated for the initial labeling, but a single US licensed dentist adjudicated the labels to establish the reference standard.
  • Qualifications: US licensed dentists labeled the data, and a US licensed dentist adjudicated those labels.

4. Adjudication Method for the Test Set:

• Standalone Performance Test Set: Ground-truthed by three US board-certified dentists. The specific adjudication method (e.g., consensus, majority) is not explicitly detailed beyond "ground-truthed by three...".
• Clinical Performance (MRMC) Test Set: US licensed dentists labeled the data, and a US licensed dentist adjudicated those labels to establish a reference standard. This implies a consensus or expert-review model, possibly 2+1 or similar, where initial labels were reviewed and finalized by a single adjudicator.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, What was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance:

• Yes, an MRMC comparative effectiveness study was done.
• Hypothesis Tested:
  • H₀: AFROC FOMₐᵢdₑd - AFROC FOMᵤₙₐᵢdₑd ≤ 0
  • H₁: AFROC FOMₐᵢdₑd - AFROC FOMᵤₙₐᵢdₑd > 0
• Effect Size:
  • Across 8 Videa Dental AI Suspect Dental Finding indications in the clinical study, the average amount of aided improvement over unaided performance was 0.002%.
  • For the caries and periapical radiolucency VDA indications (with a second operating point), clinicians had statistically significant improvement in detection performance regardless of the operating point used. The specific AFROC FOM delta is not provided for these, only that it was statistically significant.

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

• Yes, a standalone performance assessment was conducted.
• It measured and reported the performance of Videa Dental AI by itself, in the absence of any interaction with a dental professional in identifying regions of interest for all suspect dental finding, historical treatment, and normal anatomy VDA indications.

7. The Type of Ground Truth Used:

• Expert Consensus/Review: The ground truth for both standalone and clinical studies was established by US board-certified or licensed dentists who labeled and/or adjudicated the findings on the radiographs.

8. The Sample Size for the Training Set:

• The document does not explicitly state the sample size for the training set. It mentions the AI algorithms were "trained with that patient population" and "trained with bitewing, periapical and panoramic radiographs," but gives no specific number of images or patients for the training dataset.

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

• The document does not explicitly state how the ground truth for the training set was established. It only broadly states that the AI algorithms were trained with a specific patient population and image types. Given the general practice for medical AI, it can be inferred that expert labeling similar to the test set would have been used, but this is not confirmed in the provided text.

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VELMENI for DENTISTS (V4D) is a concurrent-read, computer-assisted detection software intended to assist dentists in the clinical detection of dental caries, fillings/restorations, fixed prostheses, and implants in digital bitewing, periapical, and panoramic radiographs of permanent teeth in patients 15 years of age or older. This device provides additional information for dentists in examining radiographs of patients' teeth. This device is not intended as a replacement for a complete examination by the dentist or their clinical judgment that considers other relevant information from the image, patient history, or actual in vivo clinical assessment. Final diagnoses and patient treatment plans are the responsibility of the dentist.

This device includes a Predetermined Change Control Plan (PCCP).

V4D software medical device comprises of the following key components:

• Web Application Interface delivers front-end capabilities and is the point of interaction between the device and the user.
• Machine Learning (ML) Engine delivers V4D's core ML capabilities through the radiograph type classifier, condition detection module, tooth numbering module, and merging module.
• Backend API allows interaction between all the components, as defined in this section, in order to fulfill the user's requests on the web application interface.
• Queue receives and stores messages from Backend API to send to AI-Worker.
• AI-Worker accepts radiograph analysis requests from Backend API via the Queue, passes gray scale radiographs to the ML Engine in the supported extensions (jpeg and png), and returns the ML analysis results to the Backend API.
• Database and File Storage store critical information related to the application, including user data, patient profiles, analysis results, radiographs, and associated data.

The following non-medical interfaces are also available with VELMENI for DENTISTS (V4D):

• VELMENI BRIDGE (VB) acts as a conduit enabling data and information exchange between Backend API and third-party software like Patient Management or Imaging Software
• Rejection Review (RR) module captures the ML-detected conditions rejected by dental professionals to aid in future product development and to be evaluated in accordance with VELMENIs post-market surveillance procedure.

This device includes a Predetermined Change Control Plan (PCCP).

This 510(k) clearance letter for VELMENI for DENTISTS (V4D) states that the proposed device is unchanged from its predicate (VELMENI for Dentists cleared under K240003), except for the inclusion of a Predetermined Change Control Plan (PCCP). Therefore, all performance data refers back to the original K240003 clearance. The provided document does not contain the specific performance study details directly, but it references their applicability from the predicate device.

Based on the provided text, the response will extract what details are available and note where specific information is not included in this document, but referred to as existing from the predicate device's clearance.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document refers to the acceptance criteria and performance data existing from the predicate device (K240003). It also mentions that the PCCP updates the acceptance criteria for Sensitivity, Specificity, and Average False Positives to match the lower bounds of the confidence interval demonstrated by the originally cleared models' standalone results. However, the specific values for these criteria and the reported performance are not explicitly stated in this document.

Note: The document only states that MRMC results concluded the effectiveness of the V4D software in assisting readers to identify more caries and identify more fixed prostheses, implants, and restorations correctly. Specific quantitative performance metrics (e.g., Sensitivity, Specificity, AUC, FROC, etc.) are not provided in this document.

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

The document states:

• "The new models will be evaluated on a combined test dataset with balanced ratio of historical and new data for validation to avoid overfitting historical data from repeated use."
• "The new test data is fully independent on a site-level from training/tuning data, and the test dataset remains at least 50% US data."

Specific sample size for the test set is not provided in this document.
Data Provenance: At least 50% US data, including both historical and new data. It is a retrospective dataset for testing as it uses both historical and new data collected implicitly beforehand.

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

The document does not specify the number of experts used and their qualifications for establishing ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not specify the adjudication method used for the test set (e.g., 2+1, 3+1, none).

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

Yes, an MRMC comparative effectiveness study was done.

The document states: "MRMC results concluded the effectiveness of the V4D software in assisting readers to identify more caries and identify more fixed prostheses, implants, and restorations correctly."

Effect Size: The document does not provide a specific quantitative effect size of how much human readers improve with AI vs. without AI assistance. It only makes a qualitative statement about improved identification of conditions.

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was done.

The document states: "The acceptance criteria for Sensitivity, Specificity and Average False Positives have been updated to match the lower bounds of confidence interval demonstrated by the originally cleared models' standalone results." This implies that standalone performance metrics were evaluated for the original clearance.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data). However, for a dental imaging device assisting dentists, it is highly likely that expert consensus from dental professionals (dentists or dental radiologists) would have been used for establishing ground truth. The mention of "dental professionals" rejecting ML-detected conditions in the "Rejection Review (RR)" module also hints at expert review for ground truth establishment.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set. It mentions "new and existing training and tuning data" for re-training.

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

The document does not explicitly state how the ground truth for the training set was established. However, given the context of a medical device aiding dentists in clinical detection, it is highly probable that ground truth would have been established through expert annotations or consensus from qualified dental professionals.

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Second Opinion® CS is a computer aided detection ("CADe") software to aid in the detection and segmentation of caries in periapical radiographs.

It is designed to aid dental health professionals to review periapical radiographs of permanent teeth in patients 12 years of age or older as a second reader.

Second Opinion CS detects suspected carious lesions and presents them as an overlay of segmented contours. The software highlights detected caries with an overlay and provides a detailed analysis of the lesion's overlap with dentine and enamel, presented as a percentage. The output of Second Opinion CS is a visual overlay of regions of the input radiograph which have been detected as potentially containing caries. The user can hover over the caries detection to see the segmentation analysis.

Second Opinion PC consists of three parts:

• Application Programing Interface ("API")
• Machine Learning Modules ("ML Modules")
• Client User Interface ("Client")

The processing sequence for an image is as follows:

1. Images are sent for processing via the API
2. The API routes images to the ML modules
3. The ML modules produce detection output
4. The UI renders the detection output

The API serves as a conduit for passing imagery and metadata between the user interface and the machine learning modules. The API sends imagery to the machine learning modules for processing and subsequently receives metadata generated by the machine learning modules which is passed to the interface for rendering.

Second Opinion CS uses machine learning to detect and segment caries. Images received by the ML modules are processed yielding detections which are represented as metadata. The final output is made accessible to the API for the purpose of sending to the UI for visualization. Detected carious lesions are displayed as overlays atop the original radiograph which indicate to the practitioner which teeth contain which detected carious lesions that may require clinical review. The clinician can toggle over the image to highlight a potential condition for viewing. Further, the clinician can hover over the detected caries to show a hover information box containing the segmentation of the caries in the form of percentages.

Here's a detailed breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter for Second Opinion® CS:

Acceptance Criteria and Reported Device Performance

Study Details

1. Sample Size for Test Set and Data Provenance

• Standalone Test Set: 1250 periapical radiograph images containing 404 overall caries lesions on 286 abnormal images.
  • Provenance: Retrospective. Data was collected from various geographical regions within the United States: Northwest (11.0%), Northeast (18.8%), South (29.2%), West (15.6%), and Midwest (25.5%).
  • Demographics: Includes radiographs from females (50.1%), males (44.6%), and unknown gender (5.3%). Age distribution: 12-18 (12.3%), 18-75 (81.7%), and 75+ (6.0%).
  • Imaging Devices: Carestream-Trophy KodakRVG6100 (25.7%), Carestream-Trophy RVG5200 (3.2%), Carestream-Trophy RVG6200 (27.0%), DEXIS Platinum (19.2%), DEXIS Titanium (18.8%), KodakTrophy KodakRVG6100 (0.8%), and unknown devices (5.3%).
• MRMC Test Set: 330 radiographs with 508 caries lesions across 179 abnormal images.
  • Provenance: Not explicitly stated but inferred to be retrospective, similar to the standalone study, given the focus on existing image characteristics.

2. Number of Experts and Qualifications for Test Set Ground Truth

• Standalone Study: Not explicitly stated for the standalone study. However, the MRMC study description clarifies the method for ground truth establishment, which likely applies to the test set used for standalone evaluation as well.
• MRMC Study: Ground truth was determined by four experienced dentists.
  • Qualifications: "U.S.-certified dentists" and "experienced dentists."

3. Adjudication Method for Test Set

• Standalone Study: Not explicitly stated, but implies expert consensus was used to establish ground truth.
• MRMC Study: Consensus was achieved when the Jaccard index was ≥0.4 amongst the four experienced dentists. This indicates a form of consensus-based adjudication where a certain level of agreement on lesion boundaries was required.

4. MRMC Comparative Effectiveness Study

• Yes, a fully crossed multi-reader multi-case (MRMC) study was done.
• Effect Size (Improvement of human readers with AI vs. without AI assistance):
  • Overall Caries Detection (wAFROC-FOM): Aided readers showed a significant improvement of 0.05 (95% CI: 0.01–0.09) in wAFROC-FOM compared to unaided readers.
  • Lesion-level Sensitivity: Aided readers showed a significant increase of 0.20 (95% CI: 0.16–0.24) in lesion-level sensitivity.
  • False Positives Per Image (FPPI): FPPI increased slightly by 0.16 (95% CI: -0.03–0.36).
  • Surface-level Specificity: Decreased marginally by -0.02 (95% CI: -0.04–0.00).

5. Standalone (Algorithm Only) Performance Study

• Yes, a standalone performance assessment was done to validate the inclusion of a new caries lesion anatomical segmentation.
• Key Results:
  • Sensitivity was > 70%, with an estimated lesion level sensitivity of 0.88 (95% CI), which was statistically significant (p < 0.0001).
  • Caries subgroup sensitivity for enamel was 0.95 (95% CI: 0.92, 0.97) and for dentin was 0.86 (95% CI: 0.81, 0.90).
  • Caries subgroup FPPI for enamel was 0.76 (95% CI: 0.70, 0.83) and for dentin was 0.48 (95% CI: 0.43, 0.52).
  • The standalone performance of the CAD for overall caries outperformed unaided readers in terms of wAFROC-FOM and sensitivity, albeit with higher FPPI.

6. Type of Ground Truth Used

• Expert Consensus: For the MRMC study, the ground truth was established by four experienced dentists achieving consensus (Jaccard index ≥0.4). This expert consensus is implied for the standalone study's ground truth as well.

7. Sample Size for Training Set

• The document does not explicitly state the sample size for the training set. It mentions the device uses "computer vision neural network algorithms, developed from open-source models using supervised machine learning techniques," implying a training phase, but the size of the training data is not provided.

8. How Ground Truth for Training Set Was Established

• The document states that the software uses "supervised machine learning techniques," which inherently requires a labeled dataset for training. However, it does not explicitly describe the method by which the ground truth for this training set was established (e.g., number of experts, their qualifications, or adjudication methods). It can be inferred that a similar expert labeling process was used, but details are not provided in this clearance letter.

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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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Second Opinion® Pediatric is a computer aided detection ("CADe") software to aid in the detection of caries in bitewing and periapical radiographs.

The intended patient population of the device is patients aged 4 years and older that have primary or permanent teeth (primary or mixed dentition) and are indicated for dental radiographs.

Second Opinion Pediatric is a radiological, automated, computer-assisted detection (CADe) software intended to aid in the detection and segmentation of caries on bitewing and periapical radiographs. The device is not intended as a replacement for a complete dentist's review or their clinical judgment which considers other relevant information from the image, patient history, or actual in vivo clinical assessment.

Second Opinion Pediatric consists of three parts:

• Application Programing Interface ("API")
• Machine Learning Modules ("ML Modules")
• Client User Interface (UI) ("Client")

The processing sequence for an image is as follows:

1. Images are sent for processing via the API
2. The API routes images to the ML modules
3. The ML modules produce detection output
4. The UI renders the detection output

The API serves as a conduit for passing imagery and metadata between the user interface and the machine learning modules. The API sends imagery to the machine learning modules for processing and subsequently receives metadata generated by the machine learning modules which is passed to the interface for rendering.

Second Opinion® Pediatric uses machine learning to detect caries. Images received by the ML modules are processed yielding detections which are represented as metadata. The final output is made accessible to the API for the purpose of sending to the UI for visualization. Detected caries are displayed as polygonal overlays atop the original radiograph which indicate to the practitioner which teeth contain detected caries that may require clinical review. The clinician can toggle over the image to highlight a potential condition for viewing. Further, the clinician can hover over the detected caries to show a hover information box containing the segmentation of the caries in the form of percentages.

Here's a breakdown of the acceptance criteria and study details for the Second Opinion® Pediatric device, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Study Details

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

   • Test Set Sample Size: 1182 radiographic images, containing 1085 caries lesions on 549 abnormal images.
   • Data Provenance: Not specified in the provided document (e.g., country of origin, retrospective or prospective). However, it states it was a "standalone retrospective study."

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

   • Number of Experts: Not explicitly stated.
   • Qualifications of Experts: Not specified. The document only mentions "Ground Truth," but details on the experts who established it are absent.

3. Adjudication method for the test set:

   • Adjudication Method: Not explicitly stated. The document refers to "Ground Truth" but does not detail how potential disagreements among experts (if multiple were used) were resolved. It previously mentions "consensus truthing method" for the predicate device's study, which might imply a similar approach, but it is not confirmed for the subject device.

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:

   • MRMC Study: No, an MRMC comparative effectiveness study was not performed for the Second Opinion® Pediatric device (the subject device). The provided text states, "The effectiveness of Second Opinion® Pediatric was evaluated in a standalone performance assessment to validate the CAD." The predicate device description mentions its purpose is to "aid dental health professionals... as a second reader," which implies an assistive role, but no MRMC data on human reader improvement with AI assistance is provided for either the subject or predicate device.

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

   • Standalone Study: Yes, a standalone performance assessment was explicitly conducted for the Second Opinion® Pediatric device. The study "assessed the sensitivity of caries detection of Second Opinion® Pediatric compared to the Ground Truth."

6. The type of ground truth used:

   • Ground Truth Type: Expert consensus is implied, as the study compared the device's performance against "Ground Truth" typically established by expert review. For the predicate, it explicitly mentions "consensus truthing method." It does not specify if pathology or outcomes data were used.

7. The sample size for the training set:

   • Training Set Sample Size: Not specified in the provided document. The document focuses on the validation study.

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

   • Training Set Ground Truth Establishment: Not specified in the provided document.

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Lung AI software device is a Computer-Aided Detection (CADe) tool designed to assist in the detection of consolidation/atelectasis and pleural effusion during the review of lung ultrasound scans.

The software is an adjunctive tool to alert users to the presence of regions of interest (ROI) with consolidation/atelectasis and pleural effusion within the analyzed lung ultrasound cine clip.

Lung AI is intended to be used on images collected from the PLAPS point, in accordance with the BLUE protocol.

The intended users are healthcare professionals who are trained and qualified in performing lung ultrasound and routinely perform lung ultrasounds as part of their current practice in a point-of-care environment—namely Emergency Departments (EDs). The device was not designed and tested with use environments representing EMTs and military medics.

Lung AI is not intended for clinical diagnosis and does not replace the healthcare provider's judgment or other diagnostic tests in the standard care for lung ultrasound findings. All cases where a Chest CT scan and/or Chest X-ray is part of the standard of care should undergo these imaging procedures, irrespective of the device output.

The software is indicated for adults only.

Lung AI is a Computer-Aided Detection (CADe) tool designed to assist in the analysis of lung ultrasound images by suggesting the presence of consolidation/atelectasis and pleural effusion in a scan. This adjunctive tool is intended to aid users to detect the presence of regions of interest (ROI) with consolidation/atelectasis and pleural effusion. However, the device does not provide a diagnosis for any disease nor replace any diagnostic testing in the standard of care.

The lung AI module processes Ultrasound cine clips and flags any evidence of pleural effusion and/or consolidation/atelectasis present without aggregating data across regions or making any patient-level decisions. For positive cases, a single ROI per clip from a frame with the largest pleural effusion (or consolidation/atelectasis) is generated as part of the device output. Moreover, the ROI output is for visualization only and should not be relied on for precise anatomical localization. The final decision regarding the overall assessment of the information from all regions/clips remains the responsibility of the user. Lung AI is intended to be used on clips collected only from the PLAPS point, in accordance with the BLUE protocol.

Lung AI is developed as a module to be integrated by another computer programmer into their legally marketed ultrasound imaging device. The software integrates with third-party ultrasound imaging devices and functions as a post-processing tool. The software does not include a built-in viewer; instead, it works within the existing third-party device interface.

Lung AI is validated to meet applicable safety and efficacy requirements and to be generalizable to image data sourced from ultrasound transducers of a specific frequency range.

The device is intended to be used on images of adult patients undergoing point-of-care (POC) lung ultrasound scans in the emergency departments due to suspicion of pleural effusion and/or consolidation/atelectasis. It is important to note that patient management decisions should not be made solely on the results of the Lung AI analysis.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) clearance letter for Lung AI (LAI001).

Acceptance Criteria and Device Performance for Lung AI (LAI001)

The Lung AI (LAI001) device underwent both standalone performance evaluation and a multi-reader, multi-case (MRMC) study to demonstrate its safety and effectiveness.

1. Table of Acceptance Criteria and Reported Device Performance

The document specifies performance metrics based on the standalone evaluation (sensitivity and specificity for detection and localization) and the MRMC study (AUC, sensitivity, and specificity for human reader performance with and without AI assistance). The acceptance criteria for the MRMC study are explicitly stated as an improvement of at least 2% in overall reader performance (AUC-ROC).

Standalone Performance Metrics (Derived from "Summary of Lung AI performance" and "Summary of Lung AI localization performance")

Note: Specific numerical acceptance criteria for standalone performance are not explicitly stated in the document, but the reported values demonstrated meeting the required performance for FDA clearance.

MRMC Study Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for Test Set

• Sample Size for Standalone Test Set: 465 lung scans from 359 unique patients.

• Data Provenance: Retrospectively collected from 6 imaging centers in the U.S. and Canada, with more than 50% of the data coming from U.S. centers. The dataset was enriched with abnormal cases (at least 30% abnormal per center) and included diverse demographic variables (gender, age 21-96, ethnicity).

• Sample Size for MRMC Test Set: 322 unique patients (cases). Each of the 6 readers analyzed 748 cases per reading period, for a total of 4488 cases overall.

3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set)

• Number of Experts: Two US board-certified experts initially, with a third expert for adjudication.
• Qualifications of Experts: Experienced in point-of-care ultrasound, reading lung ultrasound scans, and diagnostic radiology.

4. Adjudication Method for Test Set

• Method: In cases of disagreement between the first two experts, a third expert provided adjudication. This is a "2+1" (primary readers + adjudicator) method.

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

• Was it done?: Yes, an MRMC study was conducted.
• Effect Size of Improvement:
  • Pleural Effusion:
    • AUC improved by 0.035 (ΔAUC-PLEFF = 0.035) when aided by the device.
    • Sensitivity improved by 0.18 (ΔSe-PLEFF = 0.18) when aided by the device.
    • Specificity slightly decreased by -0.03 when aided by the device.
  • Consolidation/Atelectasis:
    • AUC improved by 0.028 (ΔAUC-CONS = 0.028) when aided by the device.
    • Sensitivity improved by 0.16 (ΔSp-CONS = 0.16) when aided by the device.
    • Specificity slightly decreased by -0.008 when aided by the device.

6. Standalone (Algorithm Only) Performance Study

• Was it done?: Yes, the "Bench Testing" section describes a standalone performance evaluation.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus (established by two US board-certified experts with a third adjudicator) for the presence/absence of consolidation/atelectasis and pleural effusion per cine clip. They also provided bounding box annotations for localization ground truth.

8. Sample Size for Training Set

• Sample Size: 3,453 ultrasound cine clips from 1,036 patients.

9. How Ground Truth for Training Set Was Established

• The document states that the underlying deep learning models were "trained on a diverse dataset of 3,453 ultrasound cine clips from 1,036 patients." While it doesn't explicitly detail the process for establishing ground truth for the training set, it can be inferred that a similar expert review process, likely involving radiologists or expert sonographers, was used, as is standard practice for supervised deep learning in medical imaging. The clinical confounders mentioned (Pneumonia, Pulmonary Embolism, CHF, Tamponade, Covid19, ARDS, COPD) suggest a robust labeling process to differentiate findings.

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Second Opinion PC is a computer aided detection ("CADe") software to aid dentists in the detection of periapical radiolucencies by drawing bounding polygons to highlight the suspected region of interest.

It is designed to aid dental health professionals to review periapical radiographs of permanent teeth in patients 12 years of age or older as a second reader.

Second Opinion PC (Periapical Radiolucency Contouring) is a radiological, automated, computer-assisted detection (CADe) software intended to aid in the detection of periapical radiolucencies on periapical radiographs using polygonal contours. The device is not intended as a replacement for a complete dentist's review or their clinical judgment which considers other relevant information from the image, patient history, or actual in vivo clinical assessment.

Second Opinion PC consists of three parts:

• Application Programing Interface ("API")
• Machine Learning Modules ("ML Modules")
• Client User Interface ("Client")

The processing sequence for an image is as follows:

1. Images are sent for processing via the API
2. The API routes images to the ML modules
3. The ML modules produce detection output
4. The UI renders the detection output

The API serves as a conduit for passing imagery and metadata between the user interface and the machine learning modules. The API sends imagery to the machine learning modules for processing and subsequently receives metadata generated by the machine learning modules which is passed to the interface for rendering.

Second Opinion PC uses machine learning to detect periapical radiolucencies. Images received by the ML modules are processed yielding detections which are represented as metadata. The final output is made accessible to the API for the purpose of sending to the UI for visualization. Detected periapical radiolucencies are displayed as polygonal overlays atop the original radiograph which indicate to the practitioner which teeth contain which detected periapical radiolucencies that may require clinical review. The clinician can toggle over the image to highlight a potential condition for viewing.

Here's a detailed breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance Study

The Pear Inc. "Second Opinion Periapical Radiolucency Contours" (Second Opinion PC) device aims to aid dentists in detecting periapical radiolucencies using polygonal contours, functioning as a second reader. The device's performance was evaluated through a standalone clinical study demonstrating non-inferiority to its predicate device, which used bounding boxes.

1. Table of Acceptance Criteria and Reported Device Performance

The submission document primarily focuses on demonstrating non-inferiority to the predicate device rather than explicitly stating pre-defined acceptance criteria with specific thresholds for "passing." However, the implicit acceptance criteria are that the device is non-inferior to its predicate (Second Opinion K210365) in detecting periapical radiolucencies when using polygonal contours.

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

• Sample Size: 500 unique unannotated periapical radiographs.
• Data Provenance: The dataset is characterized by a representative distribution across:
  • Geographical Regions (within the United States):
    • Northwest: 116 radiographs (23.2%)
    • Southwest: 46 radiographs (9.2%)
    • South: 141 radiographs (28.2%)
    • East: 84 radiographs (16.8%)
    • Midwest: 113 radiographs (22.6%)
  • Patient Cohorts (Age Distribution):
    • 12-18 years: 4 radiographs (0.8%)
    • 18-75 years: 209 radiographs (41.8%)
    • 75+ years: 8 radiographs (1.6%)
    • Unknown age: 279 radiographs (55.8%)
  • Imaging Devices: A variety of devices were used, including Carestream-Trophy (RVG6100, RVG5200, RVG6200), DEXIS (DEXIS, DEXIS Platinum, KaVo Dental Technologies DEXIS Titanium), Kodak-Trophy KodakRVG6100, XDR EV71JU213, and unknown devices.
• Retrospective or Prospective: Not explicitly stated, but the description of "representative distribution" and diverse origins suggests a retrospective collection of existing images.

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

• Number of Experts: Four expert readers.
• Qualifications of Experts: Not explicitly stated beyond "expert readers."

4. Adjudication Method for the Test Set

• Adjudication Method: Consensus approach based on agreement among at least three out of four expert readers (3+1 adjudication).

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

• Was an MRMC study done? No, a traditional MRMC comparative effectiveness study was not performed for the subject device (Second Opinion PC).
• Effect Size of Human Readers with AI vs. without AI: Not applicable for this specific study of Second Opinion PC. The predicate device (Second Opinion K210365) did undergo MRMC studies, demonstrating statistically significant improvement in aided reader performance for that device. The current study focuses on the standalone non-inferiority of Second Opinion PC compared to its predicate.

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

• Was a standalone study done? Yes, a standalone clinical study was performed. The study compared the performance of Second Opinion PC (polygonal localization) directly with Second Opinion (bounding box localization) in detecting periapical radiolucencies.
• Metrics: wAFROC-FOM and HR-ROC-AUC were used.
• Key Finding: Second Opinion PC was found to be non-inferior to Second Opinion.

7. The Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. The ground truth (GT) was established by the consensus of at least three out of four expert readers who independently marked periapical radiolucencies using the smallest possible polygonal contour.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not explicitly mentioned in the provided text. The document focuses on the clinical validation (test set).

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

• Ground Truth for Training Set: Not explicitly mentioned in the provided text. It is implied that the device was "developed using machine learning techniques" from "open-source models using supervised machine learning," which typically requires a labeled training set, but specifics on its establishment are absent.

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