Automated Planning in Preoperative Software for Shoulder Arthroplasty Decreases Time and Effort in Implant Preparation
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By
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Pierre Mahe
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Sarah Shank
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Arthur de Gast
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Maud Reynier
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February 1, 2026
Clinical Report: Automated Planning in Preoperative Software for Shoulder Arthroplasty
Overview
The study evaluates the impact of automated planning (AP) software on the time and effort required for preoperative planning in shoulder arthroplasty. Results indicate that AP significantly reduces planning time and user actions compared to manual planning.
Background
Preoperative planning is critical in shoulder arthroplasty to ensure accurate implant positioning and sizing, which can influence surgical outcomes. Traditional methods are often time-consuming and prone to human error. The introduction of automated planning tools aims to streamline this process, potentially improving efficiency and accuracy in surgical preparations.
Data Highlights
{'AP_Group_Planning_Time': 'Data needed', 'AP_Group_User_Actions': 'Data needed', 'Manual_Group_Planning_Time': 'Data needed', 'Manual_Group_User_Actions': 'Data needed'}Key Findings
- Automated planning significantly reduces the time required for preoperative planning in shoulder arthroplasty.
- Surgeons using AP performed fewer user actions compared to those using manual planning.
- The study analyzed a large dataset from real-world surgical cases to ensure statistical reliability.
- AP assists in implant positioning based on machine learning algorithms trained on expert surgeon data.
- Surgeon experience with the software influenced planning time, indicating a learning curve.
Clinical Implications
The findings suggest that automated planning tools can enhance efficiency in preoperative planning for shoulder arthroplasty, particularly benefiting low-volume surgeons. Implementing such technologies may lead to improved surgical outcomes by minimizing planning errors and reducing preparation time.
Conclusion
Automated planning in shoulder arthroplasty represents a significant advancement in preoperative preparation, potentially transforming surgical workflows and enhancing patient care.
Related Resources & Content
- Mahe P, Shank S, de Gast A, Reynier M, 2026 -- Auto-planning in preoperative shoulder arthroplasty software reduces implant planning time and number of actions
- Reliability Assessment of Three-Dimensional Preoperative Planning in Total Knee Arthroplasty: Intra- and Inter-Operator Variability, 2024 -- Springer
- Accuracy of Predicted Osteotomy Plans Utilizing Patient-Specific Instruments in Cadaveric Total Knee Arthroplasty: A Descriptive Study, 2017 -- Springer
- Exploring the Use of Augmented Reality for Accurate Placement of Base Plate Components in Reverse Total Shoulder Arthroplasty: A Feasibility Assessment, 2020 -- Springer
- Glenoid baseplate position in reverse shoulder arthroplasty - PubMed
- Comparison of patient-specific instrumentation, navigation, and mixed reality technologies for accurate glenoid positioning in reverse total shoulder arthroplasty: a systematic review and meta-analysis - PubMed
- Automated Advanced Planning for Resection in Bone Tumor Surgery
- Glenoid baseplate position in reverse shoulder arthroplasty - PubMed
- Comparison of patient-specific instrumentation, navigation, and mixed reality technologies for accurate glenoid positioning in reverse total shoulder arthroplasty: a systematic review and meta-analysis - PubMed
- Auto-planning in preoperative shoulder arthroplasty software reduces implant planning time and number of actions - Pierre Mahe, Sarah Shank, Arthur de Gast, Maud Reynier, 2026
Based on findings from:
Automated Planning in Preoperative Software for Shoulder Arthroplasty Decreases Time and Effort in Implant Preparation
Pierre Mahe, Sarah Shank, Arthur de Gast, Maud Reynier. Digital Health, 2026.
https://journals.sagepub.com/doi/10.1177/20552076261418902
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.
