Degenerative disease of the lumbar spine is a common and increasingly prevalent condition that is often implicated as the primary reason for chronic low back pain and the leading cause of disability in the western world. Surgical management of lumbar degenerative disease has historically been approached by way of open surgical procedures aimed at decompressing and/or stabilizing the lumbar spine. Advances in technology and surgical instrumentation have led to minimally invasive surgical techniques being developed and increasingly used in the treatment of lumbar degenerative disease. Compared to the traditional open spine surgery, minimally invasive techniques require smaller incisions and decrease approach-related morbidity by avoiding muscle crush injury by self-retaining retractors, preventing the disruption of tendon attachment sites of important muscles at the spinous processes, using known anatomic neurovascular and muscle planes, and minimizing collateral soft-tissue injury by limiting the width of the surgical corridor. The theoretical benefits of minimally invasive surgery over traditional open surgery include reduced blood loss, decreased postoperative pain and narcotics use, shorter hospital length of stay, faster recover and quicker return to work and normal activity. This paper describes the different minimally invasive techniques that are currently available for the treatment of degenerative disease of the lumbar spine.
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http://dx.doi.org/10.12998/wjcc.v3.i1.1 | 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
January 2025
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.
Background: Percutaneous Endoscopic Lumbar Discectomy (PELD) is a leading minimally invasive technique for lumbar disc herniation (LDH). The two primary approaches, transforaminal (PETD) and interlaminar (PEID), each present distinct advantages and challenges in treating L5-S1 LDH. This study aims to compare the efficacy and safety of these two approaches.
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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