Computer-assisted Surgical Planning Research Articles

Automated coordinate system estimation: A preliminary step toward computer-assisted radial head arthroplasty planning.

The success of radial head arthroplasty (RHA) relies on the design of the implant and precision of the surgical technique, with preoperative planning potentially playing a crucial role. The accurate establishment of a patient-specific anatomical coordinate system (ACS) is essential for this planning process. This study tested the hypothesis that an innovative automated method would be an accurate, reliable, and efficient framework to determine the ACS of the proximal radius, which would be a step toward improving the precision of RHA planning. We used advanced computational techniques to analyze 50 forearm CT scans, comparing the accuracy, reproducibility, reliability, and efficiency of the automated method with manually derived ACS using expert observers as benchmarks. The results showed that the automated approach was more accurate in identifying anatomical landmarks, with smaller mean distance discrepancies (0.6 mm) than manual observers (1 mm). Its reproducibility was also superior, with narrower reproducibility limits, particularly for ulnar notch landmarks (0.6 to 0.8 mm compared to manual selection 1.2 to 1.4 mm) (p = .01). In addition, the limits of agreement and the mean absolute rotational and translational differences of the axes were narrower for the automated method, which also reduced the construction time to an average of 46 s compared to 150 s manually (p < .001). These findings suggest that the automated method has the potential to enhance the accuracy and efficiency of preoperative and postoperative computer-assisted procedures for RHA. Further research is needed to fully understand the utility of this automated system for enhancing RHA computer-assisted surgical planning.

3D Surface-Closed Mesh Clipping Based on Polygonal Partitioning for Surgical Planning.

How to create an efficient and accurate interactive tool for triangular mesh clipping is one of the key problems to be solved in computer-assisted surgical planning. Although the existing algorithms can realize three-dimensional model clipping, problems still remain unsolved regarding the flexibility of clipping paths and the capping of clipped cross-sections. In this study, we propose a mesh clipping algorithm for surgical planning based on polygonal convex partitioning. First, two-dimensional polygonal regions are extended to three-dimensional clipping paths generated from selected reference points. Second, the convex regions are partitioned with a recursive algorithm to obtain the clipped and residual models with closed surfaces. Finally, surgical planning software with the function of mesh clipping has been developed, which is capable to create complex clipping paths by normal vector adjustment and thickness control. The robustness and efficiency of our algorithm have been demonstrated by surgical planning of craniomaxillofacial osteotomy, pelvis tumor resection and cranial vault remodeling.

Pre-prosthetic Schuchardt Osteotomy: The contribution of computer-assisted surgical planning

Premature loss of teeth leads to an inevitable egression of opposing teeth. Schuchardt osteotomy is a quick and efficient alternative to create a prosthetic space compatible with prosthetic rehabilitation. Its planning and execution are now facilitated by the rise of digital technology and specialized softwares. Through virtual simulation of the osteotomy and the creation of surgical guides, they offer enhanced safety and predictability. Communication between the practitioner and the laboratory, as well as between the practitioner and the patient, is strengthened. In this clinical case, we will detail the key steps in digital planning, and the surgical protocol for a segmental Schuchardt osteotomy for preprosthetic purposes.

Basal osteotomy of the first metacarpal using patient-specific guides and instrumentation: biomechanical and 3D CT-based analysis

IntroductionThe aim of this study was to investigate the radiological outcomes of proximal closing metacarpal extension osteotomies using patient-specific guides and instruments (PSI) in early-stage trapeziometacarpal osteoarthritis to gain further insight into the joint loading surface and the benefits of the procedure.MethodsIn a prospective observational study, nine patients were included between 11/2020 and 12/2021, undergoing a total of ten proximal metacarpal extension osteotomies for basal thumb osteoarthritis. Computer-assisted surgical planning was performed using computed tomography (CT) and three-dimensional (3D) segmentation, allowing the fabrication of 3D-printed PSIs for surgical treatment. Inclusion criteria were a 1-year follow-up by CT to assess postoperative correction of the positional shift of the first metacarpal (MC1) and the location of peak loads compared with the preoperative situation.ResultsRadiographic analysis of the peak loading zone revealed a mean displacement on the articular surface of the trapezius of 0.4 mm ± 1.4 mm to radial and 0.1 mm ± 1.2 mm to palmar, and on the articular surface of the MC1 of 0.4 mm ± 1.4 mm to radial and 0.1 mm ± 1.2 mm to dorsal.ConclusionThere were trends indicating that a flatter pressure distribution and a dorsal shift of the peak loading zone may contribute to an improvement in subjective pain and patient satisfaction associated with this surgical procedure. The non-significant radiological results and the minor dorsal-radial shifts in our small study group limit a firm conclusion.Level of evidenceIII.

Cutting guides in mandibular tumor ablation: Are we as accurate as we think?

PurposeTumor margin status is critical in local tumor recurrence and is a significant prognostic factor in head and neck cancer survival. With the introduction of computer-assisted surgical planning, one of the main challenges is the accurate positioning of the surgical cutting guide but there is limited evidence of the accuracy of the 3D cutting guides in mimicking virtually planned osteotomy. This study evaluates the accuracy of osteotomy lines produced by 3D-printed cutting guides and assesses the overall accuracy of mandibular reconstruction. Material and MethodsThe pre and postoperative 3D models were aligned using an automated surface registration feature based on the iterative closest point algorithm. The differences in osteotomy line deviation, linear and angle measurements, and 3D volume quantification of the pre and post models were measured. ResultsWe included 14 patients (8 men and 6 women with ages ranging from 13 to 75 years) with a segmental mandibular resection who met all of the inclusion criteria. The smallest defect size was 4.4 cm, the largest defect was 12.2 cm, and the average was 7.30 cm +/- 2.80 cm. The average deviation between virtually planned osteotomy and actual surgical osteotomy was 1.52 +/-1.02 mm. No covariates were associated with increased inaccuracy of the 3D-printed cutting guides. ConclusionThe finding of this study suggests that virtual surgical planning is an unambiguous paradigm shift in the predictability of the surgical plan and achievement of the reconstruction goals. The 3D-printed cutting guides are a very accurate and reliable tool in translating virtual ablation plans to an actual surgical resection margin.

Facial aesthetic surgery: Enhancing beauty through maxillofacial procedures

Facial aesthetic surgery aims to restore or improve facial harmony by addressing specific concerns and enhancing desirable features. This article explores the importance of facial harmony in our perception of beauty and attractiveness, highlighting the impact it has on an individual's self-esteem and confidence. Common surgical procedures such as rhinoplasty, genioplasty, blepharoplasty, facelifts, and lip augmentation are discussed, emphasizing their ability to enhance facial harmony and proportions. The article also examines recent technological advancements in facial aesthetic surgery, including minimally invasive techniques, computer-assisted surgical planning, and 3D imaging, which contribute to more precise outcomes. Considerations and potential risks associated with facial aesthetic surgery are addressed, emphasizing the importance of qualified surgeons, thorough evaluation, and open communication. The conclusion highlights the evolution of facial aesthetic surgery, its ability to enhance natural beauty, and its growing significance in the field of oral and maxillofacial surgery.

Feasibility study for the automatic surgical planning method based on statistical model

PurposeIn this study, we proposed establishing an automatic computer-assisted surgical planning approach based on average population models.MethodsWe built the average population models from humerus datasets using the Advanced Normalization Toolkits (ANTs) and Shapeworks. Experiments include (1) evaluation of the average population models before surgical planning and (2) validation of the average population models in the context of predicting clinical landmarks on the humerus from the new dataset that was not involved in the process of building the average population model. The evaluation experiment consists of explained variation and distance model. The validation experiment calculated the root-mean-square error (RMSE) between the expert-determined clinical ground truths and the landmarks transferred from the average population model to the new dataset. The evaluation results and validation results when using the templates built from ANTs were compared to when using the mean shape generated from Shapeworks.ResultsThe average population models predicted clinical locations on the new dataset with acceptable errors when compared to the ground truth determined by an expert. However, the templates built from ANTs present better accuracy in landmark prediction when compared to the mean shape built from the Shapeworks.ConclusionThe average population model could be utilized to assist anatomical landmarks checking automatically and following surgical decisions for new patients who are not involved in the dataset used to generate the average population model.

Fast and versatile platform for pedicle screw insertion planning.

Computer-assisted surgical planning methods help to reduce the risks and costs in transpedicular fixation surgeries. However, most methods do not consider the speed and versatility of the planning as factors that improve its overall performance. In this work, we propose a method able to generate surgical plans in minimal time, within the required safety margins and accounting for the surgeon's personal preferences. The proposed planning module takes as input a CT image of the patient, initial-guess insertion trajectories provided by the surgeon and a reduced set of parameters, delivering optimal screw sizes and trajectories in a very reduced time frame. The planning results were validated with quantitative metrics and feedback from surgeons. The whole planning pipeline can be executed at an estimated time of less than 1min per vertebra. The surgeons remarked that the proposed trajectories remained in the safe area of the vertebra, and a Gertzbein-Robbins ranking of A or B was obtained for 95 % of them. The planning algorithm is safe and fast enough to perform in both pre-operative and intra-operative scenarios. Future steps will include the improvement of the preprocessing efficiency, as well as consideration of the spine's biomechanics and intervertebral rod constraints to improve the performance of the optimisation algorithm.

Surgical Management for Vertical Maxillary Excess.

Orthognathic surgery is an effective approach to correct vertical maxillary excess (VME), which is a common maxillofacial deformity and exhibits excessive vertical development of maxilla. This review summarizes different clinical features of total, anterior and posterior VME, as well as corresponding surgical managements guided by preoperative computer-assisted surgical planning. The virtual simulation will do favor to the final determination of individual surgical plans to achieve satisfactory outcomes. Finally, a typical clinical case will be presented to demonstrate the surgical management of VME.

Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning.

The aim of this review was to investigate the application of artificial intelligence (AI) in maxillofacial computer-assisted surgical planning (CASP) workflows with the discussion of limitations and possible future directions. An in-depth search of the literature was undertaken to review articles concerned with the application of AI for segmentation, multimodal image registration, virtual surgical planning (VSP), and three-dimensional (3D) printing steps of the maxillofacial CASP workflows. The existing AI models were trained to address individual steps of CASP, and no single intelligent workflow was found encompassing all steps of the planning process. Segmentation of dentomaxillofacial tissue from computed tomography (CT)/cone-beam CT imaging was the most commonly explored area which could be applicable in a clinical setting. Nevertheless, a lack of generalizability was the main issue, as the majority of models were trained with the data derived from a single device and imaging protocol which might not offer similar performance when considering other devices. In relation to registration, VSP and 3D printing, the presence of inadequate heterogeneous data limits the automatization of these tasks. The synergy between AI and CASP workflows has the potential to improve the planning precision and efficacy. However, there is a need for future studies with big data before the emergent technology finds application in a real clinical setting. The implementation of AI models in maxillofacial CASP workflows could minimize a surgeon's workload and increase efficiency and consistency of the planning process, meanwhile enhancing the patient-specific predictability.

The Accuracy of Computer-Assisted Surgical Planning in Predicting Soft Tissue Responses After Le Fort I Osteotomy: Retrospective Analysis.

Mismatch between preoperative planning and surgical outcome in maxillofacial surgery relate to on-table replication of presurgical planning and predictive algorithm inaccuracy: software error was hereby decoupled from planning inaccuracy to assess a commercial software. The hypothesis was that soft tissue prediction error would be minimized if the surgical procedure was replicated precisely as planned and is independent of the extent of bone repositioning. Cone-beam computed tomography scans of 16 Le Fort I osteotomy patients were collected at Boston Children's Hospital. Preoperative and postoperative models of bone and soft tissue were constructed and the maxilla repositioning was replicated. Each model was subdivided into 6 regions: mouth, nose, eyes, and cheeks. Soft tissue prediction (performed using Proplan CMF-Materialise) for each patient was compared with the relative postoperative reconstruction and error was determined. P <0.05 was considered significant. Le Fort I segment repositioning was replicated within 0.70±0.18mm. The highest prediction error was found in the mouth (1.49±0.77mm) followed by the cheeks (0.98±0.34mm), nose (0.86±0.23mm), and eyes (0.76±0.32). Prediction error on cheeks correlated significantly with mouth ( r =0.63, P < 0.01) and nose ( r =0.67, P < 0.01). Mouth prediction error correlated with total advancement ( r =0.52, P =0.04). ProPlan CMF is a useful outcome prediction tool; however, accuracy decreases with the extent of maxillary advancement even when errors in surgical replication are minimized.

Cutting guides in Mandibular Tumor Ablation: Are We as Accurate as We Think?

The introduction of computer-assisted surgical planning and computer-aided manufacturing in reconstructive surgery has been proven to have many advantages such as decreased operative and ischemic times and superior accuracy.1-3 However, even with the introduction of computer-assisted surgical planning, one of the main challenges is accurate positioning of the surgical cutting guide due to lack of anatomical landmarks and soft tissue interferences during placement. The purpose of this study is to determine the accuracy of osteotomy lines produced by the virtually planned 3-dimensionally printed cutting guides and to assess the overall accuracy of mandibular resection using virtual planning and patient-specific reconstruction plates.

Laparoscopic parenchyma-sparing liver resection for large (≥ 50 mm) colorectal metastases

BackgroundTraditionally, patients with large liver tumors (≥ 50 mm) have been considered for anatomic major hepatectomy. Laparoscopic resection of large liver lesions is technically challenging and often performed by surgeons with extensive experience. The current study aimed to evaluate the surgical and oncologic safety of laparoscopic parenchyma-sparing liver resection in patients with large colorectal metastases.MethodsPatients who primarily underwent laparoscopic parenchyma-sparing liver resection (less than 3 consecutive liver segments) for colorectal liver metastases between 1999 and 2019 at Oslo University Hospital were analyzed. In some recent cases, a computer-assisted surgical planning system was used to better visualize and understand the patients’ liver anatomy, as well as a tool to further improve the resection strategy. The surgical and oncologic outcomes of patients with large (≥ 50 mm) and small (< 50 mm) tumors were compared. Multivariable Cox-regression analysis was performed to identify risk factors for survival.ResultsIn total 587 patients met the inclusion criteria (large tumor group, n = 59; and small tumor group, n = 528). Median tumor size was 60 mm (range, 50–110) in the large tumor group and 21 mm (3–48) in the small tumor group (p < 0.001). Patient age and CEA level were higher in the large tumor group (8.4 μg/L vs. 4.6 μg/L, p < 0.001). Operation time and conversion rate were similar, while median blood loss was higher in the large tumor group (500 ml vs. 200 ml, p < 0.001). Patients in the large tumor group had shorter 5 year overall survival (34% vs 49%, p = 0.027). However, in the multivariable Cox-regression analysis tumor size did not impact survival, unlike parameters such as age, ASA score, CEA level, extrahepatic disease at liver surgery, and positive lymph nodes in the primary tumor.ConclusionLaparoscopic parenchyma-sparing resections for large colorectal liver metastases provide satisfactory short and long-term outcomes.Graphical abstract

Fully automatic segmentation of craniomaxillofacial CT scans for computer-assisted orthognathic surgery planning using the nnU-Net framework.

To evaluate the performance of the nnU-Net open-source deep learning framework for automatic multi-task segmentation of craniomaxillofacial (CMF) structures in CT scans obtained for computer-assisted orthognathic surgery. Four hundred and fifty-three consecutive patients having undergone high-resolution CT scans before orthognathic surgery were randomly distributed among a training/validation cohort (n = 300) and a testing cohort (n = 153). The ground truth segmentations were generated by 2 operators following an industry-certified procedure for use in computer-assisted surgical planning and personalized implant manufacturing. Model performance was assessed by comparing model predictions with ground truth segmentations. Examination of 45 CT scans by an industry expert provided additional evaluation. The model's generalizability was tested on a publicly available dataset of 10 CT scans with ground truth segmentation of the mandible. In the test cohort, mean volumetric Dice similarity coefficient (vDSC) and surface Dice similarity coefficient at 1mm (sDSC) were 0.96 and 0.97 for the upper skull, 0.94 and 0.98 for the mandible, 0.95 and 0.99 for the upper teeth, 0.94 and 0.99 for the lower teeth, and 0.82 and 0.98 for the mandibular canal. Industry expert segmentation approval rates were 93% for the mandible, 89% for the mandibular canal, 82% for the upper skull, 69% for the upper teeth, and 58% for the lower teeth. While additional efforts are required for the segmentation of dental apices, our results demonstrated the model's reliability in terms of fully automatic segmentation of preoperative orthognathic CT scans. • The nnU-Net deep learning framework can be trained out-of-the-box to provide robust fully automatic multi-task segmentation of CT scans performed for computer-assisted orthognathic surgery planning. • The clinical viability of the trained nnU-Net model is shown on a challenging test dataset of 153 CT scans randomly selected from clinical practice, showing metallic artifacts and diverse anatomical deformities. • Commonly used biomedical segmentation evaluation metrics (volumetric and surface Dice similarity coefficient) do not always match industry expert evaluation in the case of more demanding clinical applications.

Three-dimensional printing in the field of oral and maxillofacial surgery.

Advanced imaging techniques and modalities coupled with computer-assisted surgical planning and simulation has been in use in the field of medicine. However, it is worth noting that it is now being frequently used for the evaluation and exploration of the craniofacial structures. It had gained ingress in the planning as well as forecasting of the surgical outcomes of oral and maxillofacial surgical interventions. Numerous surgical guides and devices which are tailor-made can be fabricated using three-dimensional (3D) printing technology. The article is intended to put forth an overview of 3D printing technology and its applications in the field of oral and maxillofacial surgery.

Adherence to Computer-Assisted Surgical Planning in 136 Maxillofacial Reconstructions.

ObjectiveTo investigate the adherence to initially planned maxillofacial reconstructions using computer-assisted surgery (CAS) and to identify the influential factors affecting its compliance for maxillofacial reconstruction.Patients and MethodsA retrospective analysis of 136 computer-assisted maxillofacial reconstructive surgeries was conducted from January 2014 to June 2020. The categorical parameters involved age, gender, disease etiology, disease site, defect size, bone flap segments, and flap type. Apart from descriptive data reporting, categorical data were related by applying the Fisher-exact test, and a p-value below 5% was considered statistically significant (P < 0.05).ResultsThe main reasons for partial or non-adherence included unfitness, patient health condition, and other subjective reasons. Out of the total patient population, 118 patients who underwent mandibular reconstruction showed higher CAS compliance (83.9%) compared to the 18 midface reconstruction (72.2%) without any statistically significant difference (p = 0.361). Based on the size of the defect, a significantly higher CAS compliance (p = 0.031) was observed with a minor defect (80.6%) compared to the large-sized ones (74.1%). The bone flaps with two or more segments were significantly (p = 0.003) prone to observe a partial (15.4%) or complete (12.8%) discard of the planned CAS compared to the bone flaps with less than two segments. The malignant tumors showed the lowest CAS compliance when compared to other disorders without any significant difference (p = 0.1).ConclusionThe maxillofacial reconstructive surgical procedures offered optimal compliance to the initially planned CAS. However, large-sized defects and multiple bone flap segments demonstrated a higher risk of partial or complete abandonment of the CAS.

Computer-assisted surgical planning of complex bone tumor resections improves negative margin outcomes in a sawbones model.

Several technologies have been implemented in orthopedic surgery to improve surgical outcomes, usually focusing on more accurate execution of a surgical plan, but the development of the plan itself is also of great importance. The purpose of this study is to examine whether the use of preoperative computer planning platforms can improve the surgical plan? Eight surgeons created a preoperative surgical plan to resect a distal femur parosteal osteosarcoma in two settings: (1) Using a 2-D and 3-D CT scan only (current standard); and (2) using a computer-assisted planning platform. The plans were thereafter virtually executed using a novel surgical navigation system and a Sawbones model. This simulated model was derived from, and identical to, an actual patient scenario. The outcomes of interest were the number of positive margin cuts, and the volume of the resected specimen. Using the surgical plan developed with computer assistance, there were 4 positive margin cuts made by 2 surgeons. In comparison, using standard planning, there were 14 positive margin cuts made by all 8 surgeons (p = 0.02). The resection volume was larger in the computer-assisted plans (96 ± 10 mm3) than in the standard plans (88 ± 7 mm3) (p = 0.055). Computer-assisted planning significantly decreased the risk of a positive margin resection in this Sawbones tumor model used to simulate resection of a primary bone sarcoma. This proof of concept study highlights the importance of advanced surgical planning and sets the ground for developing beneficial surgical planning systems.

Computer-assisted open reduction internal fixation of intraarticular radius fractures navigated with patient-specific instrumentation, a prospective case series

BackgroundIntra-articular fractures are associated with posttraumatic arthritis if inappropriately treated. Exact reduction of the joint congruency is the main factor to avoid the development of arthrosis. Aim of this study was to evaluate feasibility of computer-assisted surgical planning and 3D-printed patient-specific instrumentation (PSI) for treatment of distal intraarticular radius fractures.Method7 Patients who suffered a distal intraarticular radius fracture were enrolled in this prospective case series. Preoperative CT-scan was recorded, whereupon a 3D model was computed for surgical planning and design of PSI for surgical navigation. Postoperative accuracy and joint congruency were assessed. Patients were followed-up 3, 6 and 12 months postoperatively.ResultsMean follow-up was 16 months. Over all range of motion was restored and flexion, extension and pronation showed significant recovery, p < 0.05. Biggest intraarticular joint step-off and gap reduced from average 2.49 (± 1.04) to 0.8 mm (± 0.44), p < 0.05 and 6.12 mm (± 1.04) to 2.21 mm (± 1.16), p < 0.05. Average grip strength restored (3–16 months) from 20.33 kg (± 7.12) to 39.3 kg (± 19.55) p < 0.05, 100% of the healthy contralateral side. 3D-accuracy for guided fragments was 2.07 mm (± 0.64) and 8.59° (± 2.9) and 2.33 mm (± 0.69) and 12.86° (± 7.13), p > 0.05 for fragments reduced with ligamentotaxis.ConclusionComputer-assisted and PSI navigated intraarticular radius fracture treatment is feasible, safe and accurate. The benefits of this method, however, do not outstand the additional effort.Level of evidence IV.

Accurate and Automatic 3D Segmentation of Femur and Pelvis from CT Images of the Hip based on Deep Learning

Background: Prosthesis design of hip joint and computer-assisted surgical planning can benefit from segmentation-based computed tomography (CT). Purpose: To automatically segment the three-dimensional (3D) hip joint images, the authors developed a deep learning-based segmentation algorithm and verified its feasibility with CT image of hip joint. Methods: Conventional image augmentation strategies and specific image augmentation strategies, which were designed to mimic the deformed shape and blurring boundaries of the diseased hips, were applied to obtain a large number of training samples of diseased hips to avoid overfitting. A 3D segmentation algorithm named light 3D U-net, which segmented images from coarse to fine, was developed to improve the segmentation accuracy and reduce the computation time in 3D hip joint image with multiple targets. Results and Discussion: The test results showed that the proposed method would exceed 0.9 in the aspects of Dice score, Specificity, and Sensitivity. Comparing with traditional method, the proposed method is more efficient, accurate, and robust. The proposed method has shown great potential to be applied in prosthesis design and computer-assisted surgical planning.

MRI-based 3D models of the hip joint enables radiation-free computer-assisted planning of periacetabular osteotomy for treatment of hip dysplasia using deep learning for automatic segmentation

IntroductionBoth Hip Dysplasia(DDH) and Femoro-acetabular-Impingement(FAI) are complex three-dimensional hip pathologies causing hip pain and osteoarthritis in young patients. 3D-MRI-based models were used for radiation-free computer-assisted surgical planning. Automatic segmentation of MRI-based 3D-models are preferred because manual segmentation is time-consuming.To investigate(1) the difference and(2) the correlation for femoral head coverage(FHC) between automatic MR-based and manual CT-based 3D-models and (3) feasibility of preoperative planning in symptomatic patients with hip diseases. MethodsWe performed an IRB-approved comparative, retrospective study of 31 hips(26 symptomatic patients with hip dysplasia or FAI). 3D MRI sequences and CT scans of the hip were acquired. Preoperative MRI included axial-oblique T1 VIBE sequence(0.8 mm3 isovoxel) of the hip joint. Manual segmentation of MRI and CT scans were performed. Automatic segmentation of MRI-based 3D-models was performed using deep learning. Results(1)The difference between automatic and manual segmentation of MRI-based 3D hip joint models was below 1 mm(proximal femur 0.2 ± 0.1 mm and acetabulum 0.3 ± 0.5 mm). Dice coefficients of the proximal femur and the acetabulum were 98 % and 97 %, respectively. (2)The correlation for total FHC was excellent and significant(r = 0.975, p < 0.001) between automatic MRI-based and manual CT-based 3D-models. Correlation for total FHC (r = 0.979, p < 0.001) between automatic and manual MR-based 3D models was excellent.(3)Preoperative planning and simulation of periacetabular osteotomy was feasible in all patients(100 %) with hip dysplasia or acetabular retroversion. ConclusionsAutomatic segmentation of MRI-based 3D-models using deep learning is as accurate as CT-based 3D-models for patients with hip diseases of childbearing age. This allows radiation-free and patient-specific preoperative simulation and surgical planning of periacetabular osteotomy for patients with DDH.