GAUCI – iBV

Digital surgery merges two complementary aspects:

- Computational surgery, which is fed by patient and imaging data, enabling the creation of 3D/4D geometric and biomechanical models to simulate and plan surgery before it is performed.

- Augmented surgery, which is based on a more or less complex digital model and enables planning to be applied using intraoperative assistance and guidance tools such as 3D printing (model, instrumentation, implant), navigation, robotics or mixed reality including collaborative cloud platform.

Assessing the clinical relevance of these digital solutions alone is not enough to demonstrate their usability and interest in a complex environment such as the operating room or interventional procedure room. The development of models adapted to surgical needs, and the analysis of multiparametric data derived from the use of these models' application tools, should enable us to optimize the integration of these tools in the operating room and in daily practice.

The ICARE laboratory's strategy is to create reliable automated numerical models, notably using ML or AI tools, that will be used in the diagnosis, decision making process and surgical procedure. To assess their usability through various application tools, we are carrying out crash tests in real life conditions and developing multi-parametric usability matrices for digital solutions. These results will serve as reliable indicators of a digital tool's ability to integrate into a surgical workflow.

The ICARE laboratory is at the interface between a research and development activity involving researchers and industrial partners, and an application activity carried out by surgeons on test platforms (anatomy laboratory, augmented operating room, simulators) and in operating rooms. The ICARE team is working on 3D model of bone especially shoulder, forearm, distal radius and pelvis in order to provide preoperative reliable and automated model to treat bone fractures. Statistical shape models are also developed to retrieve the premorbid bone and joint and to guide the objective of the surgery.
The ICARE laboratory's activities are part of a 360° assessment of the use of digital tools in the operating room, which is the only way to guarantee their performance. Human factors are considered, whether subjective (mental workload, situational awareness or acceptability) or objective (heart rate, sweating, etc.) during the procedure. We aim to develop indicators to predict the usability of numeric surgical devices.
The use of data generated using digital tools is also at the heart of ICARE's strategy. We are also promoting the industrialization of high-quality data collection to feed our algorithms, in close collaboration with the Côte d'Azur healthcare data warehouse.

Humerus model

Traumatology is a major public health scourge worldwide, and the third leading cause of global morbidity according to the WHO (50% of emergency consultations and 400 000 hospitalizations a year).

Fractures of the proximal humerus are the 3rd most common fracture and therefore represent a major public health issue (loss of independence in the elderly, disability in young, active patients).

We are developing personalized, collaborative preoperative planning for trauma surgery on the proximal humerus to minimize operative complications and recovery time.

This includes:

- Creation of a generative model (of the variational auto-encoder or adversarial generative network type) of the proximal humerus fracture, making it possible to generate a representation of a fractured humerus from the representation of an average non-fractured humerus (and vice versa).

- Creation of a generative model (anatomical variability of the humerus)

- Automated segmentation of the fractured head of the humerus from CT images of the shoulder. The difficulty arises from the fragmentation of the humeral head into multiple pieces that vary according to the nature of the fracture.

- Estimating the reduced shape of the proximal humerus

Distal Radius

Fractures of the distal radius account for 20% of all fractures. The diversity of fracture types requires specific management of each patient. We are developing a standard 3D model of the forearm including the distal radius by detecting 3D anatomical reference systems in a reproducible and automatable way to eliminate user dependency on reference system placement and improve joint characterization. There are several applications for the model, such as implementation in software to make a precise diagnosis, guide the therapeutic decision and plan surgery by selecting the most suitable osteosynthesis plates. Intraoperative application is made possible by the generation of 3D-printed patient-specific guides and the use of mixed reality.

Human factors in the intraoperative use of mixed reality

We aim to investigate the role of physician’s acceptability towards MR and rendering tools and the impact on professionals’ mental workload perception. Indeed, this research investigation will mobilize a conceptual framework grounded on Information System Theory and backed by personal-based characteristics. So, the technological acceptance and individual attitudes (self-efficacy, work relevance, anxiety) are used. Acceptability assessment with adapted scores completes the reflection, reinforcing the understanding of practices and attitudes. Subjective, physiological, and behavioral measures are collected. Structural equation models and generalized additive models are be used to perform associations and causal relationships between variables. This methodological approach is particularly relevant for measuring, estimating, and testing research models and linear and non-linear relations between concepts simultaneously. This methodology is specifically adapted to assess complex models studied in a real-world environment (impacted by causal relationship between variables).

Perioperative (pre-, intra- and post-operative) data collection for translational research: the fraCTure project

Results in orthopedics and trauma surgery is highly related to experience. Orthopedics and Traumatology present specific technological barriers of shortening the time between planning and procedure, reducing sources of errors, internalizing tools and processes of automated planning in the care institutions and of empowering the surgeons.

Through the ongoing Côte d’Azur Health Data Warehouse project, we are building a project to industrialize the collection of data, the data quality enhancement, and the accessibility of the data to make clinical, imaging, and intraoperative necessary data available for 3D models generation for intraoperative evaluation and to associate a longitudinal follow-up of cohorts for clinical validation. For this purpose, we have established a collaboration with the HUGO health data warehouse to continuously collect the data necessary for our research and development. The same solution is used by the Côte d’Azur and the HUGO Health data Warehouse, to facilitate interactions. The final goal is to associate multiple EDS in trauma centers and well-recognized and high-volume orthopedics facilities to build an efficient network to collect data around surgery. Multiple innovative approaches such as federated learning are relevant and has been already applied in other fields (cancerology).

A specific focus is done for intraoperative data collected through mixed reality use.

Recent publications

Garret, J, Gunst, S, Gauci, MO. Posterior shoulder instability. Orthop Traumatol Surg Res. 2024; :104061. doi: 10.1016/j.otsr.2024.104061. PubMed PMID:39579966 .
Caubère, A, Rutigliano, S, Bourdon, S, Erickson, J, Morelli, M, Parsons, M et al.. The effect of humeral tray thickness on glenohumeral loads in a reverse shoulder 'smart' implant. Int Orthop. 2024;48 (11):2881-2889. doi: 10.1007/s00264-024-06282-6. PubMed PMID:39231836 .
Bige, B, Gonzalez, JF, Boileau, P, Gauci, MO. Epileptic patients with anterior shoulder instability: are there specific bone lesions? A case control study. J Shoulder Elbow Surg. 2024; :. doi: 10.1016/j.jse.2024.06.006. PubMed PMID:39111686 .
Barret, H, Garret, J, Favard, L, Bonnevialle, N, Collin, P, Gauci, MO et al.. Long-term (minimum 10 years) survival and outcomes of pyrocarbon interposition shoulder arthroplasty. J Shoulder Elbow Surg. 2024; :. doi: 10.1016/j.jse.2024.05.026. PubMed PMID:39067661 .
Trojani, MC, Gauci, MO, Cointat, C, Cabane, L, Breuil, V. Pseudotumoral calcinosis of the elbow with ulnar and radial nerve deficits. Bone Rep. 2024;22 :101779. doi: 10.1016/j.bonr.2024.101779. PubMed PMID:38988611 PubMed Central PMC11231696.
Berhouet, J, Samargandi, R, Jacquot, A, Favard, L, Boileau, P, Gauci, MO et al.. Restoration of internal rotation after reverse shoulder arthroplasty may vary depending on etiology in patients younger than 60 years of age: a multicenter retrospective study. J ISAKOS. 2024;9 (4):620-627. doi: 10.1016/j.jisako.2024.05.016. PubMed PMID:38851325 .
Micicoi, G, de Geofroy, B, Chamoux, J, Ghabi, A, Gauci, MO, Bernard de Dompsure, R et al.. Total blood loss after hip hemiarthroplasty for femoral neck fracture: Anterior versus posterior approach. Orthop Traumatol Surg Res. 2024;110 (5):103911. doi: 10.1016/j.otsr.2024.103911. PubMed PMID:38801888 .
Gauci, MO, Glevarec, L, Bronsard, N, Cointat, C, Pelletier, Y, Boileau, P et al.. Is preoperative 3D planning reliable for predicting postoperative clinical differences in range of motion between two stem designs in reverse shoulder arthroplasty. J Shoulder Elbow Surg. 2024;33 (8):1771-1780. doi: 10.1016/j.jse.2023.11.031. PubMed PMID:38281677 .
Werthel, JD, Dufrenot, M, Schoch, BS, Walch, A, Morvan, Y, Urvoy, M et al.. Are glenoid retroversion, humeral subluxation, and Walch classification associated with a muscle imbalance?. J Shoulder Elbow Surg. 2024;33 (7):1493-1502. doi: 10.1016/j.jse.2023.11.027. PubMed PMID:38242526 .
Gauci, MO, Ceccarelli, R, Lavoue, V, Chelli, M, van der Meijden, OAJ, Gonzalez, JF et al.. Total shoulder arthroplasty for primary glenohumeral osteoarthritis: does posterior humeral subluxation persist after correction of the glenoid version at 5 years minimum?. J Shoulder Elbow Surg. 2024;33 (7):e347-e355. doi: 10.1016/j.jse.2023.11.010. PubMed PMID:38182028 .
Werthel, JD, Dufrenot, M, Schoch, BS, Walch, A, Morvan, Y, Urvoy, M et al.. Are glenoid retroversion, humeral subluxation and Walch classification associated with a muscle imbalance. J Shoulder Elbow Surg. 2023; :. doi: 10.1016/j.jse.2023.11.002. PubMed PMID:38158039 .
Winter, R, Citarel, A, Chabrand, P, Chenel, A, Bronsard, N, Poujade, T et al.. An Evaluation of the Reliability of Manual Landmark Identification on 3D Segmented Wrists. J Bone Joint Surg Am. 2024;106 (4):315-322. doi: 10.2106/JBJS.23.00173. PubMed PMID:37995208 .
Maiotti, M, De Nigris, S, Tardioli, S, Cavaliere, M, Barillà, S, Angelini, A et al.. IODA-shoulder®, a 3-dimensional automated software for glenoid bone loss quantification in shoulder instability. A validation study. Orthop Traumatol Surg Res. 2023; :103745. doi: 10.1016/j.otsr.2023.103745. PubMed PMID:37918692 .
Micicoi, L, Gonzalez, JF, Gauci, MO, Chabrand, P, Machado, A, Bronsard, N et al.. Acute compartment syndrome of the lower limbs: Fasciotomy or dermofasciotomy? A cadaver study of compartment pressures. Orthop Traumatol Surg Res. 2024;110 (1):103736. doi: 10.1016/j.otsr.2023.103736. PubMed PMID:37890523 .
de Geofroy, B, Micicoi, G, Olmos, M, Boileau, P, Bronsard, N, Gonzalez, JF et al.. Early morbidity and mortality after one-stage bilateral shoulder arthroplasty. Int Orthop. 2024;48 (2):505-511. doi: 10.1007/s00264-023-06003-5. PubMed PMID:37853140 .
Boyer, L, Fond, G, Gauci, MO, Boussat, B. Regulation of medical research in France: Striking the balance between requirements and complexity. Rev Epidemiol Sante Publique. 2023;71 (4):102126. doi: 10.1016/j.respe.2023.102126. PubMed PMID:37451779 .
Ripoll, T, Chelli, M, Johnston, T, Chaoui, J, Gauci, MO, Vasseur, H et al.. Three-Dimensional Measurement of Proximal Humerus Fractures Displacement: A Computerized Analysis. J Clin Med. 2023;12 (12):. doi: 10.3390/jcm12124085. PubMed PMID:37373779 PubMed Central PMC10299604.
Berhouet, J, Samargandi, R, Favard, L, Turbillon, C, Jacquot, A, Gauci, MO et al.. The Real Post-Operative Range of Motion Differs from the Virtual Pre-Operative Planned Range of Motion in Reverse Shoulder Arthroplasty. J Pers Med. 2023;13 (5):. doi: 10.3390/jpm13050765. PubMed PMID:37240935 PubMed Central PMC10219507.
Olmos, MI, Johnston, TR, Gonzalez, JF, Camuzard, O, Gauci, MO. Glomus tumor of the scapular neck with axillary nerve compression at the shoulder. A case report. Shoulder Elbow. 2023;15 (1):61-64. doi: 10.1177/17585732211040160. PubMed PMID:36895604 PubMed Central PMC9990104.
Gauci, MO, Olmos, M, Cointat, C, Chammas, PE, Urvoy, M, Murienne, A et al.. Validation of the shoulder range of motion software for measurement of shoulder ranges of motion in consultation: coupling a red/green/blue-depth video camera to artificial intelligence. Int Orthop. 2023;47 (2):299-307. doi: 10.1007/s00264-022-05675-9. PubMed PMID:36574021 .

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Marc-Olivier GAUCI

Intelligence in Computational and Augmented surgery, Reuse of healthcare data, multiparametric Evaluation (ICARE)

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