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

ConditionHip Prosthesis

Trial statusRecruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

Age18+

SponsorPhilipp Fürnstahl

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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

Disqualifiers

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

Trial population

We plan to record 60 THA surgeries in the operating room performed by different hip surgeons at Balgrist University Hospital, Kantonsspital Baden, or Kantonsspital Winterthur, for data collection. Patients are recruited if they fit the in-/exclusion criteria and get primary information from the surgeon as soon as the decision for surgery has been made in the outpatient clinic. The patients are handed a copy of the informed consent and can decide on participating until the follow-up consultation. The decision to perform the procedure is not linked to the study. The decision to participate does not affect the procedure itself.

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

Recording of total hip arthroplasty
Other intervention
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.

Treatment groups

60 Participants

are divided into 1 treatment group

Group A: Project population for total hip arthroplasty surgery recordings1 intervention

Trial outcomes

Primary outcomes

labeled recordings of total hip arthroplasty

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.

Time frame

Up to 1 year

Other outcomes

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

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).

Time frame

Up to 1 year

Sponsors and contacts

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Philipp Fürnstahl

Lead sponsor

Balgrist University Hospital

Sponsor institution

Kantonsspital Baden

Collaborator

Kantonsspital Winterthur KSW