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OTS Hip is indicated to enable planning of orthopedic surgical procedures based on CT and X-Ray medical imaging data of the patient anatomy. It is an intraoperative image-guided localization system that enables navigated surgery. It links a freehand probe, tracked by a passive marker sensor system, to virtual computer image space on a patient's preoperative image data being processed by the OTS platform.

The system is indicated for orthopedic hip surgical procedures where a reference to a rigid anatomical structure, such as the pelvis, can be identified relative to a system generated model of the anatomy. The system aids the surgeon to accurately navigate a compatible prosthesis to the preoperatively planned position.

The system is designed for orthopedic surgical procedures including:

• Pre-operative planning of total hip arthroplasty (THA)
• Intraoperative navigated surgery for THA using a posterior approach

OTS Hip is a system to support a surgeon with preoperative planning and intraoperative guidance during orthopedic hip joint replacement surgery.

The OTS Hip device is a modified device from the company's previously cleared OTS Hip (K232140).

OTS Hip is comprised of software systems and hardware components that work together to form a stereotaxic system. The system uses medical imaging data in DICOM format that is loaded into the system for access in the software that are part of the system.

OTS Hip software consists of OTS Hip Plan (OHP), which is a 3D preoperative planning software, and OTS Hip Guide (OHG) that provides intraoperative real-time navigation for the guidance of surgical tools and prosthetic components in relation to the preoperatively determined goal positions.

OHP is a software for preoperative planning prior to a THA (Total Hip Arthroplasty) surgery. OHP enables the orthopedic surgeon to prepare surgery by analyzing the patient anatomy in a 3D environment based on medical imaging data.

OHG imports the result from the preceding planning stage, a released plan, with the 3D model and planned data, from the database of the OTS system. In addition, OHG monitors the real-time information of the position of instruments and prosthetic components in a 3D environment by means of medical imaging data.

The components of the OHG device include a camera and computer stand with an electrical system to which a camera and a medical panel PC are attached, a footswitch, a keyboard, Tracers (passive markers), adapters that hold the Tracers and can be mounted to compatible surgical instruments and that are used for calibration, and tools and instruments that are used during surgery.

The OTS Hip is compatible with the following components:

• PINN GB OFFSET GRATER HANDLE, DePuy Synthes 2550-00-100
• Emphasys offset reamer, DePuy Synthes 4811-00-510
• Greatbatch Offset Cup Impactor, DePuy Synthes 2550-00-115
• Pinnacle straight impactor, DePuy Synthes 2217-50-041
• Emphasys straight impactor, DePuy Synthes 4812-00-150
• Kincise Pinnacle Straight Shell Impactor, DePuy Synthes 2000-02-002 (long)
• Kincise Pinnacle Straight Shell Impactor, DePuy Synthes 2000-02-012 (short)
• Kincise Emphasys Straight Shell Impactor, DePuy Synthes 2000-03-001 (long)
• Kincise Emphasys Straight Shell Impactor, DePuy Synthes 2000-03-012 (short)

The provided text describes two validation studies for the OTS Hip's machine learning algorithms: "CT Algorithm Segmentation and Landmark Validation" and "Sacral Slope Landmark Identification Algorithm Validation." The acceptance criteria and the studies proving the device meets these criteria are detailed below for each.

1. CT Algorithm Segmentation and Landmark Validation

Acceptance Criteria:

• The ML-models overall met the acceptance criteria (specific numerical criteria are not detailed in the provided text).
• Accuracy of Machine Learning (ML) algorithms for segmentation and landmark identification.

Study Proving Acceptance:

Additional Information on the Study:

• Sample size for the test set and data provenance:
  • Sample Size: 90 datasets.
  • Data Provenance:
    • Country of Origin: US (45.6%), Japan (33.3%), and the European Union (21.1%).
    • Retrospective/Prospective: Not explicitly stated, though "data collected from human cadaver studies" for "system level validation" might hint at existing data. For the ML validation, it mentions "cases were then separated into training and testing datasets," suggesting a retrospective approach from a collected dataset.
    • The OUS dataset was unblinded.
    • The data from Japan included a high percentage of dysplastic hips with accompanying marked degenerative change, representing a worst-case scenario.
    • Images from multiple CT equipment manufacturers were included.
    • Independent datasets were used between training and testing for OUS datasets, though collected from the same site.
• Number of experts used to establish the ground truth for the test set and qualifications of those experts:
  • Number of Experts: Not explicitly stated, but "Appropriately qualified clinical experts" established the ground truth. "A third reviewer" was used for validation.
  • Qualifications: "Appropriately qualified clinical experts."
• Adjudication method for the test set:
  • Method: "Using objective criteria, cases were evaluated by blinded annotators." "Cases assigned to the test dataset were then validated by a third reviewer who was not included in the annotation process of the training data." This indicates a form of multi-reviewer consensus/adjudication.
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not explicitly mentioned or implied.
• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Yes, the validation focused on the accuracy of the ML algorithms compared to ground truth, implying standalone performance.
• The type of ground truth used: Manually annotated "ground truth" segmentations and landmarks established by "appropriately qualified clinical experts" through a consensus-like process (blinded annotators + third reviewer validation).
• The sample size for the training set: Not explicitly stated, but distinct from the 90 test datasets.
• How the ground truth for the training set was established: Established by "Appropriately qualified clinical experts" through a process of "blinded annotators” and validation by a third reviewer. "None of the datasets used for training was used for testing."

2. Sacral Slope Landmark Identification Algorithm Validation

Acceptance Criteria:

• The ML-model met the acceptance criteria (specific numerical criteria are not detailed in the provided text).
• Accuracy of Machine Learning (ML) algorithms for Sacral Slope landmark added for x-ray images.

Study Proving Acceptance:

Additional Information on the Study:

• Sample size for the test set and data provenance:
  • Sample Size: 503 x-ray images from 276 US patients.
  • Data Provenance:
    • Country of Origin: US patients.
    • Retrospective/Prospective: Not explicitly stated, but "cases were then separated into training and testing datasets," suggesting a retrospective approach from a collected dataset.
    • Representative of the US population in terms of gender, age, and ethnicity.
    • Included images from multiple x-ray equipment manufacturers.
• Number of experts used to establish the ground truth for the test set and qualifications of those experts:
  • Number of Experts: Not explicitly stated, but "Appropriately qualified clinical experts" established the ground truth. "A third reviewer" was used for validation.
  • Qualifications: "Appropriately qualified clinical experts."
• Adjudication method for the test set:
  • Method: "Using objective criteria, cases were evaluated by blinded annotators." "Cases assigned to the test dataset were then validated by a third reviewer who was not included in the annotation process of the training data." This indicates a form of multi-reviewer consensus/adjudication.
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not explicitly mentioned or implied.
• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Yes, the validation focused on the accuracy of the ML algorithms compared to ground truth, implying standalone performance.
• The type of ground truth used: Manually annotated "ground truth" landmarks established by "appropriately qualified clinical experts" through a consensus-like process (blinded annotators + third reviewer validation).
• The sample size for the training set: Not explicitly stated, but distinct from the 503 x-ray images/276 patients in the test set.
• How the ground truth for the training set was established: Established by "Appropriately qualified clinical experts" through a process of "blinded annotators” and validation by a third reviewer. "None of the x-ray images or patients included in the training dataset were re-used in the test dataset."

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OTS Hip is indicated to enable planning of orthopedic surgical procedures based on CT medical imaging data of the patient anatomy. It is an intraoperative image-guided localization system that enables navigated surgery. It links a freehand probe, tracked by a passive marker sensor system, to virtual computer image space on a patient's preoperative image data being processed by the OTS platform.

The system is indicated for orthopedic hip surgical procedures where a reference to a rigid anatomical structure, such as the pelvis, can be identified relative to a CT-based model of the anatomy. The system aids the surgeon to accurately navigate a compatible prosthesis to the preoperatively planned position.

The system is designed for orthopedic surgical procedures including:

• Pre-operative planning of Total Hip Arthroplasty (THA)
• Intraoperative navigated surgery for THA using a posterior approach

OTS Hip is a system to support a surgeon with preoperative planning and intraoperative guidance during orthopedic hip joint replacement surgery.

OTS Hip is comprised of software systems and hardware components that work together to form a stereotaxic system. The system uses medical imaging data in DICOM format that is loaded into the system for access in the software that are part of the system.

OTS Hip software consists of OTS Hip Plan (OHP), which is a 3D preoperative planning software, and OTS Hip Guide (OHG) that provides intraoperative real-time navigation for the quidance of surgical tools and prosthetic components in relation to the preoperatively determined goal positions.

OHP is a software for preoperative planning prior to a THA (Total Hip Arthroplasty) surgery. OHP enables the orthopedic surgeon to prepare surgery by analyzing the patient anatomy in a 3D environment based on medical imaging data.

OHG imports the result from the preceding planning stage, a released plan, with the 3D model and planned data, from the database of the OTS system. In addition, OHG monitors the real-time information of the position of instruments and prosthetic components in a 3D environment by means of medical imaqinq data.

The components of the OHG device include a camera and computer stand with an electrical system to which a camera and a medical panel PC are attached, a footswitch, a keyboard, Tracers (passive markers), adapters that hold the Tracers and can be mounted to compatible surgical instruments and that are used for calibration, and tools and instruments that are used during surgery.

The document describes the performance testing and validation of the OTS Hip device, particularly focusing on its Machine Learning (ML) algorithms for segmentation and landmark identification.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The document states that the ML-models overall met the acceptance criteria for segmentation and landmark identification, though it also notes two clinically complex cases that did not pass for segmentation. While specific numerical acceptance criteria (e.g., minimum dice score for segmentation, specific distance for landmark identification) are not explicitly provided in a table, the qualitative statement indicates successful validation.

Given the information provided, a table attempting to present this would look like:

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

• Test Set Sample Size: 90 datasets
• Data Provenance:
  • Countries of Origin: US (45.6%), Japan (33.3%), and the European Union (21.1%).
  • Retrospective/Prospective: Not explicitly stated, but the description of "real-life data from surgeries under clinical conditions" and "collected from the same site" for OUS datasets suggests retrospective collection of existing CT medical imaging data.
  • Representativeness: The datasets were described as "representative of the US population in terms of gender, age, and ethnicity" and included "images from multiple CT equipment manufacturers." The data from Japan included a "high percentage of dysplastic hips with accompanying marked degenerative change."

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

• Number of Experts: Not explicitly stated as a single number. The text mentions "Appropriately qualified experts established the ground truth" and "Cases were then validated by a third reviewer who evaluated the initial annotation." This implies at least two annotators (initial) and a third for validation/adjudication.
• Qualifications of Experts: "Appropriately qualified experts." Specific qualifications (e.g., years of experience, sub-specialty) are not detailed.

4. Adjudication Method for the Test Set

• Method: The document states, "Cases were then validated by a third reviewer who evaluated the initial annotation." This suggests a form of conflict resolution or quality control, where a third expert steps in after initial annotation to confirm or correct. The exact rules (e.g., majority vote, senior expert decision) are not specified, but it implies a process where disagreements or initial annotations are reviewed. There is no mention of "2+1" or "3+1" specifically, but the "third reviewer" role aligns with adjudication.

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

• MRMC Study: The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with AI assistance vs. without AI assistance. The validation focuses on the standalone performance of the ML algorithms compared to expert ground truth.

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

• Standalone Performance: Yes, standalone performance of the ML algorithms was done. The "Machine Learning Algorithm Validation" section explicitly states that "The results of segmentation and landmark ML algorithms were compared with the manually annotated 'ground truth' segmentations and landmarks of the test dataset." This describes an algorithm-only evaluation.

7. The Type of Ground Truth Used

• Ground Truth Type: Expert consensus/manual annotation. The ground truth was established by "Appropriately qualified experts" through "manually annotated 'ground truth' segmentations and landmarks."

8. The Sample Size for the Training Set

• Training Set Sample Size: The exact sample size for the training set is not specified. The document only mentions that the "test datasets were independent from the training dataset, where none of the datasets used for training was used for testing."

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

• Training Set Ground Truth: The document states that "Cases were then separated into training and testing datasets in an unbiased fashion." It implies that the same method used for establishing ground truth for the test set (i.e., "manually annotated 'ground truth' segmentations and landmarks" by "appropriately qualified experts" with potential "third reviewer" validation) would have been applied to the data that eventually formed the training set. However, the details for the training set ground truth establishment are not explicitly elaborated further than this general statement for all cases.

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The Ortoma Treatment Solution system is intended to be an intraoperative image-guided localization system to enable navigated surgery. It links a freehand probe, tracked by a passive marker sensor system, to virtual computer image space either on a patient's preoperative image data being processed by OTS platform, or on an individual 3D-model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface.

The system is indicated for hip surgical procedures, in which the use of navigated surgery is considered to be safe and effective, and where a reference to a rigid anatomical structure, such as the skull, a long bone, or vertebra, can be identified relative to a CT-based model of the anatomy. The system aids the surgeon to accurately navigate a hip prosthesis to the preoperatively planned position.

Example orthopedic surgical procedures include but are not limited to:

Total Hip Arthroplasty (THA) using posterior approach.

Preoperative planning and intraoperative navigated surgery for joint replacement with Stryker Exeter X3 Rimfit cups.

The Ortoma Treatment Solution – OTS is a stereotactic navigation system, supporting the surgeon with positioning information in relation to the individual patient anatomy before and during orthopedic hip joint replacement surgery.

OTS consists of two software parts:

• Ortoma Hip Plan OHP, a 3D preoperative planning application .
• . Ortoma Hip Guide - OHG, provides real-time navigation for guidance of surgical tools and prosthetic components in relation to the preoperatively determined goal position

OHP has support for PACS (Picture Archiving Communication System) solutions already available at Hospitals. The PACS system handles the DICOM (Digital Imaging and Communications in Medicine) data obtained from Computed Tomography (CT) scans of patients. DICOM data from the PACS system is uploaded into the OHP and an illustrative 3D image of the unique patient´s anatomy is created.

The OHP application includes functions to define the measurements of the individual patient anatomy, such as the offset, anteversion and leg length. The surgeon selects prosthetic components in relation to the anatomy data and measurements obtained in the application, to virtually decide the position of the prosthetic components.

The OHG application functions as a positioning system in relation to the preoperatively planned goal position. The system includes passive retro-reflective markers with camera, monitor and adapters to attach the passive retro-reflective markers to surgical instruments. The OHG gives the surgeon real-time positioning information, while the orthopedic surgeon positions the surgical instruments and prosthetic components during the implant surgery, in relation to the patient anatomy as previously planned. The surgeons decide based on their clinical knowledge and experience how the surgery should be performed.

The OTS is compatible with the following Stryker components: Stryker Asnis III 4.0x20 mm full thread stainless steel, Stryker Holding Sleeve for Screwdrivers, Stryker Cannulated Screwdriver with AO Coupling - Hex 2.5mm, Stryker Ortholock Ex-Pin 150x4 mm screw, Stryker quick release apex, Stryker Reamer, and Stryker Cup Inserter.

Here's a breakdown of the acceptance criteria and the study used to demonstrate the device meets those criteria, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document explicitly states that the results for Inclination and Anteversion were "not outside the safe zone of ± 10°" and the mean deviation for Distance was "below the acceptable accuracy level of ±3mm."

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

• Sample Size for Test Set: 7 hips (from 4 human cadavers). One hip was excluded due to "unacceptable bone in the acetabulum and surrounding bone for reliable navigation."
• Data Provenance: The data was generated from a "simulated clinical conditions" study using human cadavers. Thus, it's a prospective study conducted in a controlled lab environment. The country of origin is not explicitly stated, but the sponsor "Ortoma AB" is located in Sweden, suggesting the study likely took place in Sweden or a neighboring country.

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

• The document does not explicitly state the number of experts used or their qualifications for establishing the ground truth for the cadaver study.
• However, the ground truth was established by postoperative CT-scan reference data. This implies that the 'ground truth' itself was derived instrumentally rather than through expert consensus on images. Surgical procedure (THA) and interpretation of CT scans would have been performed by qualified personnel, but they are not detailed as "experts establishing ground truth."

4. Adjudication Method for the Test Set

• The document does not describe an adjudication method involving multiple human readers for interpreting the results of the cadaver study. The "ground truth" was derived from postoperative CT scans, which are objective measurements.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described. The study focused on the device's standalone accuracy in a cadaver model guided by predefined surgical techniques (Stryker Exeter X3 Rimfit Acetabular Cup THA using a posterior approach). The effect size of human readers improving with or without AI assistance is not reported.

6. Standalone (Algorithm Only) Performance Study

• Yes, the described study is essentially a standalone performance study. The "Ortoma Treatment Solution - OTS" device's accuracy was assessed by comparing its intraoperative guidance results (inclination, anteversion, distance) against a post-operative CT-scan derived ground truth. This evaluates the algorithm's guidance performance without explicitly measuring its impact on human surgical performance.

7. Type of Ground Truth Used

• The primary ground truth used was postoperative CT-scan reference data. This is an instrumental, objective measure of the final implanted prosthesis position.

8. Sample Size for the Training Set

• The document does not provide information regarding the sample size used for the training set. The study described is a validation study, not a description of the model development or training.

9. How Ground Truth for the Training Set Was Established

• The document does not provide information on how the ground truth for the training set was established, as details about the training process are not included in this summary.

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