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The Talee and the Talee PostOp are the Cranial Remolding Orthoses intended for medical purposes for infants from 3 to 18 months of age with moderate-to-severe cranial deformities.

The Talee is used for infants from 3 to 18 months with moderate-to-severe non-synostotic positional plagiocephaly, including infants with plagiocephalic- and scaphocephalic- shaped heads and combination of these defects.

The Talee PostOp is used for infants from 3 to 18 months of age whose synostosis has been surgically corrected, but who still have cranial deformities including plagiocephalic- and scaphocephalic- shaped heads.

The Talee and the Talee PostOp are Cranial Remolding Orthoses which are individually designed and manufactured medical devices class II. The Cranial Remolding Orthosis is made individually as a patient-specific device according to the type of deformity and disposition of the patient. The Cranial Orthosis is made according to the 3D scan of the infant´s head. The modified shape of the infant's symmetrical head shape is created in CAD software from the 3D scan. CAD model of the outer shell of the Orthosis is based on modified shape of infant's head. The outer shell of the Orthosis is produced by 3D printing. The Cranial Orthosis is assembled from two-part outer 3D printed shell and the inner soft foam layer.

The provided text does not contain the detailed information required to fill out a table of acceptance criteria and reported device performance for a medical device that relies on AI or an algorithm. The submission is for a Cranial Orthosis (Talee, Talee PostOp), which is a physical device, not an AI-driven diagnostic or assistive tool.

Therefore, many of the requested points, such as "effect size of how much human readers improve with AI vs without AI assistance," "standalone (i.e. algorithm only without human-in-the-loop performance)," "number of experts used to establish ground truth," and "sample size for training set," are not applicable to this specific device submission as an AI/algorithm is not the primary component of the device being evaluated for clinical effectiveness.

The document focuses on non-clinical performance testing of the physical cranial orthosis and its manufacturing process, along with demonstrating substantial equivalence to a predicate device.

However, I can extract information related to the device's physical and material properties, and the testing conducted on those aspects, to address the spirit of the request as much as possible given the provided text.

Here's an attempt to categorize the relevant information from the document, acknowledging that it's a physical device and not an AI/algorithm:

Acceptance Criteria and Device Performance (for a Physical Cranial Orthosis)

Given that this is a 510(k) submission for a physical cranial orthosis (Talee, Talee PostOp) and not an AI/algorithm-driven device, the "acceptance criteria" and "device performance" are primarily related to its mechanical properties, manufacturing accuracy, and biocompatibility, rather than diagnostic or assistive performance metrics for an AI. The document indicates that all predetermined acceptance criteria for these tests were met.

Table of Acceptance Criteria and Reported Device Performance (Physical Device Focus)

Study Details (Focusing on non-clinical performance and substantial equivalence)

Given that this is a physical medical device (Cranial Orthosis), not an AI/algorithm, many of the requested AI-specific points are not relevant. However, I will address what is present in the document.

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

   • The document mentions "tested devices Talee and Talee PostOp" for mechanical tests, but does not specify numerical sample sizes for these non-clinical performance tests (e.g., how many orthoses were impact tested).
   • For biocompatibility, the tests describe standard animal models (rabbits, guinea pigs for irritation/sensitization) and cell cultures for cytotoxicity. No specific numerical sample sizes for these tests are provided within the document itself, but these are standard ISO methods.
   • Data Provenance: The tests were conducted to support a submission to the U.S. FDA. The data provenance for the non-clinical tests would be the testing laboratories that performed them. The document refers to "non-clinical performance testing (Software, Product, Manufacturing method)." It does not discuss patient data for algorithms (as there is no AI algorithm being cleared).

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

   • Not applicable in the context of AI/algorithm ground truth. The "ground truth" for this device relates to established physical/material standards (e.g., ISO for biocompatibility, mechanical engineering standards for strength/stiffness). The specialists involved would be testing technicians and engineers, not clinical experts establishing a ground truth for a diagnostic AI.

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

   • Not applicable as there is no human reader "adjudication" in the context of an AI/algorithm performance study. The evaluation of non-clinical tests would follow standard laboratory quality control and reporting procedures.

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 MRMC study was done, and it is not applicable. This device is a physical cranial orthosis, not an AI diagnostic or assistance tool. Therefore, there are no "human readers" whose performance is being augmented by AI.

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

   • Not applicable. The device itself is a physical product. While it uses digital scanning and CAD software in its manufacturing process, the submission does not describe an AI or algorithm as a "standalone" medical device whose performance is being evaluated in this manner. The Cranial Comparison App (CCA) is mentioned as a "separate reporting software program" for measurements, but its function as a standalone diagnostic algorithm is not the focus of this submission.

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

   • The "ground truth" for this submission is based on established industry standards and regulatory requirements for medical devices, specifically:
     • Biocompatibility standards: ISO 10993 series.
     • Mechanical properties: Engineering specifications and performance metrics.
     • Manufacturing accuracy: Dimensional tolerances and verification against CAD models.
   • There is no "clinical ground truth" in the sense of diagnosing a disease, as this is a treatment device.

7. The sample size for the training set:

   • Not applicable. This device is manufactured individually based on a 3D scan of a patient's head using CAD software and 3D printing. There is no "training set" in the context of machine learning or AI algorithm development for the device itself.
   • The CAD software (R4D CADCAM software) is presumably a commercial product and its "training" or development data would precede this submission.

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

   • Not applicable, as there is no training set for an AI/algorithm related to the device's efficacy. The manufacturing process relies on 3D scanning and CAD/CAM, which are established digital design and manufacturing technologies, not machine learning that requires a "ground truth" for a training set in this context.

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The STARband 3D is intended for medical purposes for use on infants from three to 18 months of age, with moderate to severe non-synostotic positional plagiocephaly, infants with plagiocephalic-, brachycephalic-, and scaphocephalic- shaped heads by applying mild pressure to prominent regions of the infant's cranium in order to improve cranial symmetry and/or shape.

The STARband® redirects the head growth to improve proportion and symmetry. The practitioner takes a 3-dimensional captured image of the infant's head to acquire the existing shape. The 3-dimensional positive model is modified to obtain greater symmetry and space in the areas of flattening. The STARband® provides total contact over the prominent or bossed areas of the baby's head to discourage growth there. Over the course of treatment, the inside of the band is further modified by the practitioner to provide space for growth to occur in the flat or depressed areas. The shape of the STARband® directs growth into the areas of least resistance and creates a precise pathway for the head shape to improve in symmetry and proportion.

The STARband® 3D™ cranial orthosis device proposed in this submission has identical indications for use to the predicate submission K211376. The difference with this proposed device is a structural change with a bilateral side opening design and two latch closure mechanisms at each opening. The indications for use, the intended use, and the underlying principles of operation of the STARband® cranial orthosis remain the same.

The provided text is a 510(k) summary for the STARband 3D cranial orthosis, which is a medical device for treating infants with abnormal head shapes. This document does not include information about a study that proves the device meets acceptance criteria related to AI/algorithm performance or human reader improvement, as it is not an AI-powered device.

The "acceptance criteria" and "study that proves the device meets the acceptance criteria" in this document refer to the non-clinical performance testing conducted for the STARband 3D. The device is a physical cranial orthosis, and its evaluation revolves around its physical characteristics, manufacturing process, and safety, not AI diagnostic performance.

Therefore, many of the requested points regarding AI/algorithm performance, multi-reader multi-case studies, ground truth establishment for AI, and sample sizes for training sets of AI models are not applicable to this device and the provided document.

However, I can extract information related to the physical device's performance testing and "acceptance criteria" from the document.

Here's the relevant information from the provided text, adapted to the closest interpretations of your questions for a non-AI device:

Acceptance Criteria and Device Performance (for a physical medical device)

The device is a cranial orthosis (a helmet) designed to improve cranial symmetry and/or shape in infants. The "acceptance criteria" for a physical device like this are met through demonstrating that its design, manufacturing process, and materials are safe and perform as intended, comparable to a predicate device.

Here's a table summarizing the non-clinical performance and acceptance criteria for the STARband 3D, as reported in the 510(k) summary:

Here are the answers to your specific questions, indicating where the information is not applicable (N/A) for this type of device submission:

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

   • See the table above.

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

   • Sample Size: The document refers to "test samples" for Dimensional Analysis, Fit Assessment, and Mechanical Testing, and "test samples" or "finished, fully assembled additively manufactured device" for Biocompatibility. However, specific numerical sample sizes for these tests are not provided in this 510(k) summary.
   • Data Provenance: The document does not specify the country of origin of the data or whether the studies were retrospective or prospective. Given that these are non-clinical (bench/lab) tests for a physical device, the concepts of retrospective/prospective clinical data are not directly applicable. These appear to be laboratory-based validation studies.

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. This device does not involve "ground truth" derived from expert interpretation of medical images or patient data in the typical sense of AI/diagnostic device evaluation. The "ground truth" for these tests comes from engineering specifications, known dimensions, mechanical properties of materials, and successful operation based on design intent. For example, the "unmodified patient matched mold" serves as a reference for the fit assessment.

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

   • N/A. Adjudication methods are typically relevant for clinical studies or studies where multiple human readers interpret data to establish a consensus "ground truth." This is a non-clinical, bench testing report for a physical device.

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-powered diagnostic device, and therefore, no MRMC study or assessment of human reader improvement with AI assistance was conducted or would be applicable.

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

   • N/A. This is a physical cranial orthosis, not a software algorithm.

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

   • N/A in the typical AI sense. For this physical device, "ground truth" is established by:
     • Engineering Specifications/Design Files: For Dimensional Analysis (comparison of 3D printed part to original design file).
     • Physical Models/Molds: For Fit Assessment (fitting to an "unmodified patient matched mold").
     • Predicate Device Performance: For Pressure Test (comparing pressure force to the predicate device).
     • Defined Mechanical Criteria: For Latch Closure tests (e.g., specific cycle counts, exceeding a defined pull strength threshold).
     • International Standards: For Biocompatibility (conformance to ISO 10993-5 and ISO 10993-10).

8. The sample size for the training set

   • N/A. This device does not involve machine learning or a "training set."

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

   • N/A. This device does not involve machine learning or a "training set."

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Intended for medical purposes for use on infants from 3 to 18 months of age, with moderate-to-severe non-synostotic positional plagiocephaly, including infants with plagiocephalic-, and scaphocephalic-shaped heads by applying mild pressure to prominent regions of an infant's cranium in order to improve cranial symmetry and/or shape. These devices are also indicated for adjunctive use for infants from 3 to 18 months of age synostosis has been surgically corrected, but who still have moderate-to-severe cranial deformities including plagiocephalic-, and scaphocephalic- shaped heads.

The Ottobock MyCRO Band is a non-sterile temporary orthosis to aid in the correction of head shape caused by positioning in infants three (3) to eighteen (18) months of age. As such, this device is manufactured to match patient anatomy and may only be used on the patient for which the cranial orthosis was designed. The orthosis uses contact and growth zones to guide the growth of the head. The contact zones define limits for growth, while the growth zones leave space in areas required for forming the natural head shape. An adaptable closure allows for adjustability as the child grows. The orthosis is made of thermoplastic material with a soft, washable lining on the interior.

The provided text describes a 510(k) submission for a medical device called "MyCRO Band," a cranial orthosis used for infants with positional plagiocephaly. However, the document does not contain the information necessary to describe acceptance criteria and a study proving device performance as requested in the prompt.

Specifically, the document focuses on demonstrating substantial equivalence to a predicate device (STARband®) based on similar indications for use, intended use, basic principles of operation, and basic design, along with non-clinical testing. It mentions "Scanner Evaluation utilized a worst-case challenge reference object of known dimensions to assess 3D imaging devices for adequate accuracy, repeatability, and reproducibility" and "Verification testing included dimensional analysis, fit assessment, and mechanical testing of test samples," but it does not provide:

• A table of specific acceptance criteria and reported device performance values related to clinical outcomes or direct head shape correction metrics.
• Sample sizes for a clinical test set (as the described tests are non-clinical).
• Data provenance (country of origin, retrospective/prospective).
• Information on experts establishing ground truth or adjudication methods.
• Any multi-reader multi-case (MRMC) comparative effectiveness study.
• Standalone algorithm performance (as this is a physical orthosis, not an AI/algorithm-based device).
• Type of ground truth used related to clinical efficacy.
• Training set sample size or ground truth establishment for a training set (again, not an AI/algorithm device).

The "Summary of Testing" section lists:

1. Biological evaluation per ISO 10993-1:2018
2. Cytotoxicity testing per ISO 10993-5:2009
3. Irritation and Sensitization testing per ISO 10993-10:2010
4. Scanner evaluation, including repeatability/reproducibility study
5. Verification testing

While these tests "successfully met the predetermined acceptance criteria," the specific criteria and the detailed results demonstrating the device's efficacy in correcting cranial deformities (beyond dimensional accuracy of the scanner or fit of the orthosis) are not provided in this 510(k) summary. These are typically covered in a more detailed study report that is part of the full 510(k) submission but not included in this high-level summary.

Therefore, I cannot fulfill your request for the specific details of acceptance criteria and proven device performance based solely on the provided text. The text focuses on the engineering and biocompatibility aspects for demonstrating substantial equivalence rather than a detailed clinical performance study as would be seen for an AI/algorithm device or a device with novel mechanisms of action requiring extensive clinical validation.

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