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| Name | Class |
|---|---|
| Kantonsspital Baden | OTHER |
| Kantonsspital Winterthur KSW | OTHER |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Project population for total hip arthroplasty surgery recordings | We plan to record 60 total hip arthroplasty (THA) surgeries in the operating theatre performed by different surgeons of the hip team at Balgrist University Hospital or Kantonsspital Baden or Kantonsspital Winterthur, for data collection purposes. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | Up to 1 year |
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| Measure | Description | Time Frame |
|---|---|---|
| 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). |
Inclusion Criteria:
Exclusion Criteria:
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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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Nicola Cavalcanti, MD | Contact | +41445107379 | nicola.cavalcanti@balgrist.ch | |
| Matthias Seibold, PhD | Contact | P +41 44 510 73 57 | matthias.seibold@balgrist.ch |
| Name | Affiliation | Role |
|---|---|---|
| Philipp Fürnstahl, PhD | Balgrist University Hospital | Principal Investigator |
| Patrick Zingg, MD | Balgrist University Hospital | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Kantonsspital Baden | Not yet recruiting | Baden | Canton of Aargau | 5404 | Switzerland |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35145990 | Background | Carrillo F, Esfandiari H, Muller S, von Atzigen M, Massalimova A, Suter D, Laux CJ, Spirig JM, Farshad M, Furnstahl P. Surgical Process Modeling for Open Spinal Surgeries. Front Surg. 2022 Jan 25;8:776945. doi: 10.3389/fsurg.2021.776945. eCollection 2021. | |
| 32057597 | Background | Charney M, Paxton EW, Stradiotto R, Lee JJ, Hinman AD, Sheth DS, Prentice HA. A Comparison of Risk of Dislocation and Cause-Specific Revision Between Direct Anterior and Posterior Approach Following Elective Cementless Total Hip Arthroplasty. J Arthroplasty. 2020 Jun;35(6):1651-1657. doi: 10.1016/j.arth.2020.01.033. Epub 2020 Jan 22. |
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The individual participant data will only be shared in an anonymized form upon reasonable request after the project has been completed and the data has been published as a scientific article.
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| Up to 1 year |
| Kantonsspital Winterthur | Not yet recruiting | Winterthur | Canton of Zurich | 8400 | Switzerland |
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| Balgrist University Hospital | Recruiting | Zurich | Canton of Zurich | 8008 | Switzerland |
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| 34774686 | Background | Haynes JA, Hopper RH Jr, Ho H, McDonald JF 3rd, Parks NL, Hamilton WG. Direct Anterior Approach for Primary Total Hip Arthroplasty Lowers the Risk of Dislocation Compared to the Posterior Approach: A Single Institution Experience. J Arthroplasty. 2022 Mar;37(3):495-500. doi: 10.1016/j.arth.2021.11.011. Epub 2021 Nov 10. |
| 18952942 | Background | Neumuth T, Jannin P, Strauss G, Meixensberger J, Burgert O. Validation of knowledge acquisition for surgical process models. J Am Med Inform Assoc. 2009 Jan-Feb;16(1):72-80. doi: 10.1197/jamia.M2748. Epub 2008 Oct 24. |
| 31076194 | Background | Patel NN, Shah JA, Erens GA. Current Trends in Clinical Practice for the Direct Anterior Approach Total Hip Arthroplasty. J Arthroplasty. 2019 Sep;34(9):1987-1993.e3. doi: 10.1016/j.arth.2019.04.025. Epub 2019 Apr 18. |
| Background | Seibold, M., Hoch, A., Farshad, M., Navab, N., & Fürnstahl, P. (2022). Conditional generative data augmentation for clinical audio datasets. Medical Image Computing and Computer Assisted Intervention (MICCAI). Singapore. |
| Background | Zisimopoulos, O., Flouty, E., Luengo, I., Giataganas, P., Nehme, J., Chow, A., & Stoyanov, D. (2018). DeepPhase: Surgical Phase Recognition in CATARACTS Videos. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial, 11073, 265-275. |