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NCT Number: NCT05993845

Modeling and Workflow Recognition for the Anterior Approach in Total Hip Arthroplasty

The purpose of this study is to create a systematic and general description of the surgical process for the direct anterior approach (DAA) in total hip arthroplasty (THA). For this purpose a surgical process model with a labeled dataset of THA surgery videos will be segmented into the individual surgical steps and sub-steps using a systematic approach.

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Key information

Conditions

Hip Prosthesis

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Kantonsspital Baden, Baden, Canton of Aargau, Switzerland

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About this study

BACKGROUND AND PROJECT RATIONALE

In Total Hip Arthroplasty (THA), a degenerated hip joint is replaced with an artificial acetabular and femoral component. In clinical practice, several approaches to accessing bone anatomy are employed. The direct anterior approach (DAA) has been adopted widely in recent years due to smaller incisions, fewer complications, faster recovery, and improved patient outcomes. However, the reduced space and limited access may cause more complexity in the execution of surgical steps.

Primary objective

The first goal of this project is to create a systematic and general description of the surgical process for DAA in THA. For this purpose, surgical process models (SPMs) provide a basis to manage, organize, and optimize the surgical process. For this purpose, a labeled dataset of THA surgery videos will be segmented into individual surgical steps and sub-steps using a systematic approach. SPMs are a simplified network of surgical or surgery-related activities and their relationships. These models can be used to compare several interventions, surgeons, or whole Operating Room teams. In addition, the SPMs can be used as input for workflow management systems.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Primary surgery for total hip arthroplasty (THA) at Balgrist University Hospital, Kantonsspital Baden and Kantonsspital Winterthur
  • Complete radiological data (CT and Hip/Pelvis x-ray)
  • Signed consent

Exclusion criteria

  • Missing or incomplete consent
  • Previous surgery of the hip at the site of the THA
  • Fracture of the hip or pelvis at the site of the THA

Treatment and study plan

Recording of total hip arthroplasty

Other

We record total hip arthroplasties (THA) of hip surgeons at Balgrist University Hospital (BUH), Kantonsspital Baden (KSB), Kantonsspital Winterthur (KSW). Video & audio data are stored locally at Balgrist-managed servers (patient-sensitive information removed).

Data includes:

Demographics (year of birth, sex, smoking status, body mass index) Diagnosis Surgery-specific data & other surgical procedures during recording Standard preoperative Computed tomography of the hip/pelvis Standard pelvis x-rays pre- & postoperative Standard axial hip x-rays pre- & postoperative Western Ontario & McMaster Universities Osteoarthritis Index & Harris-Hip-Score To ensure the generated Surgical process model(SPM), evaluated using the recordings obtained from surgeries performed at BUH, generally describes the surgical process of THA, we apply our model to surgeries from the KSB and the KSW. There, 3 THAs are recorded and analyzed regarding commonalities and discrepancies in the SPM.

Primary outcomes

  1. labeled recordings of total hip arthroplasty

    Time frame: Up to 1 year

    Surgery recordings of Total Hip Arthroplasties (THA) with Direct Anterior Approach (DAA) performed at Balgrist University Hospital will be used to develop an in-depth systematic analysis of surgical workflows and activity. The comparison and evaluation of THA with DAA performed at two external hospitals (Kantonsspital Baden, Switzerland and Kantonsspital Winterthur, Switzerland) will allow to generalize and evaluate the developed models: Multicenter study.

Other outcomes

  1. automated deep learning-based approach for surgical workflow and activity recognition for total hip arthroplasty (THA)

    Time frame: Up to 1 year

    With surgical process modeling (SPM) as the basis, this study aims to develop an automated deep learning-based approach for surgical workflow and activity recognition for total hip arthroplasty (THA), which has not been proposed in previous work. The labels created during the creation of the Surgical Process Model will be used to train a state-of-the-art deep neural network for surgical phase recognition. The surgical process model and the trained deep learning model can be used in future work for the in-depth analysis of surgical processes, surgical training, and skill assessment. Furthermore, automated surgical workflow recognition systems can provide vital input to physicians in the form of early warnings in cases of deviations and anomalies and context-aware decision support (Padoy, 2019), as well as the automatic extraction of a surgery's protocol, which is crucial for archiving, educational and post-operative patient-monitoring purposes (Zisimopoulos, et al., 2018).

Study contacts

Contact information is provided by the study sponsor or research team.

Matthias Seibold, PhD

CONTACT

[email protected]

P +41 44 510 73 57

Nicola Cavalcanti, MD

CONTACT

[email protected]

+41445107379

Sponsors and collaborators

Lead sponsor

Philipp Fürnstahl

Other

Collaborators

  • Kantonsspital Baden
  • Kantonsspital Winterthur KSW

Registry information

Important dates

Study start
2024
Primary completion
2026
Study completion
2026
First posted
Aug 15, 2023
Registry last updated
Jun 15, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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