Objective: To describe and evaluate minimally invasive repair of acetabular fractures in dogs using plates contoured to 3D-printed hemipelvic models.
Study Design: Ex vivo feasibility study and case report.
Sample Population: Adult canine cadavers (n = 5); 8 year old male neutered Chihuahua.
Methods: Bone plates were contoured to 3D printed hemipelvic models derived from computed tomographic scans of each dog. In cadavers, acetabular, ischial, and pubic osteotomies were performed. A small craniolateral approach to the ilial body and a caudal approach to the ischium were made and connected through epiperiosteal tunnels. Under fluoroscopic guidance, fractures were reduced, and precontoured bone plates were applied with locking screws. Postoperative computed tomographic images were used to assess fracture gaps, step defects, and pelvic angulation. Cadavers were dissected for subjective assessment of sciatic nerve injury. Radiographic and clinical follow up was acquired for the clinical case.
Results: Small fracture gaps (<2 mm) and step defects (<1 mm), low pelvic angulation (<5°), and minimal (none n = 4 and mild n = 1) sciatic nerve injuries were observed in cadaver testing. There was slight (~1 mm) medial displacement of the pubic segment and good functional outcome for the clinical case, with radiographic healing documented at 3 months postoperatively.
Conclusion: Minimally invasive acetabular fracture repair in dogs with the aid of 3D printing was feasible and accurate.
Clinical Significance: Minimally invasive repair techniques assisted by 3D printing may be applicable for acetabular fractures in dogs. The technique should be evaluated further before routine use can be recommended.
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http://dx.doi.org/10.1111/vsu.13937 | DOI Listing |
Int J Comput Assist Radiol Surg
January 2025
Advanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.
Purpose: This paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.
Methods: We propose a modified Transformer model with the architecture comprising no positional encoding, 5 fully connected layers, 1 encoder, and 3 decoders.
J Minim Invasive Gynecol
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Department of Obstetrics and Gynecology, Lankenau Medical Center, Wynnewood, Pennsylvania, USA.
Study Objective: To compare the aggregate fibroid specimen weights between abdominal and minimally invasive (MI) myomectomies to determine whether fibroid burden significantly impacts surgical approach to myomectomy.
Design: Retrospective cohort study; INTERVENTIONS: Comparison of aggregate fibroid specimen weights between abdominal and MI myomectomies SETTING: Community health care system.
Patients: 281 patients undergoing abdominal and MI myomectomies between March 2018 and December 2023.
Urology
January 2025
The Warren Alpert Medical School, Brown University, Providence, RI, USA; Minimally Invasive Urology Institute, The Miriam Hospital, Providence, RI, USA; Division of Urology, The Warren Alpert Medical School of Brown University, Providence, RI, USA.
J Clin Neurosci
January 2025
Division of Neurosurgery, Department of Surgery, Brawijaya University/Saiful Anwar General Hospital, Malang, East Java, Indonesia.
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View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Mines Saint-Etienne, Univ Jean Monnet, Etablissement Francais du Sang, INSERM, U 1059 Sainbiose, Centre CIS, F-42023, Saint-Etienne, France. Electronic address:
The rise in minimally invasive procedures has created a demand for efficient and reliable planning software to predict intra- and post-operative outcomes. Surrogate modelling has shown promise, but challenges remain, particularly in cardiovascular applications, due to the complexity of parametrising anatomical structures and the need for large training datasets. This study aims to apply statistical shape modelling and machine learning for predicting stent deployment in real time using patient-specific models.
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