prone-lateral-minimally-invasive-retropleural-corpectomy-using

Prone Lateral Minimally Invasive Retropleural Corpectomy Using a Rotatable Radiolucent Jackson Table

Prone lateral single-position spinal surgery allows simultaneous manipulation of the anterior and posterior columns, avoiding re-draping and improving ...

Chang Gung Memorial Hospital and Chang Gung University College of Medicine

Of the proposed animal interbody fusion models, rat caudal discs have gained popularity in disc research due to their strong resemblance to human discs with respect to geometry, composition and mechanical properties. The purpose of this study is to demonstrate an efficient, repeatable and easily accessible animal model of interbody fusion for future research into mechanical testing and graft materials.

Characterization of a novel caudal vertebral interbody fusion in a rat tail model: An implication for future material and mechanical testing.

Correcting the scoliosis and stabilizing the spine in the corrected position is the basis of treatment for adolescent idiopathic scoliosis (AIS). Spinal instrumentation and derotation are the principle steps of surgery for any type of AIS. A perspicuous understanding needs to be attained regarding derotation maneuvers in practice; therefore, we intend to compare radiological outcomes following concave and convex rod derotation maneuvers to analyze their efficacy to correct selective Lenke's Type-1 scoliosis.

Is Convex Derotation Equally Effective as Concave Derotation for Achieving Adequate Correction of Selective Lenke's Type- 1 Scoliosis?

We present an automated method for measuring the sagittal vertical axis (SVA) from lateral radiography of whole spine using a convolutional neural network for keypoint detection (ResUNet) with our improved localization method. The algorithm is robust to various clinical conditions, such as degenerative changes or deformities. The ResUNet was trained and evaluated on 990 standing lateral radiographs taken at Chang Gung Memorial Hospital, Linkou and performs SVA measurement with median absolute error of 1.183 ± 0.166 mm. The 5-mm detection rate of the C7 body and the sacrum are 91% and 87%, respectively. The SVA calculation takes approximately 0.2 s per image. The intra-class correlation coefficient of the SVA estimates between the algorithm and physicians of different years of experience ranges from 0.946 to 0.993, indicating an excellent consistency. The superior performance of the proposed method and its high consistency with physicians proved its usefulness for automatic measurement of SVA in clinical settings.

Artificial Intelligence for Automatic Measurement of Sagittal Vertical Axis Using ResUNet Framework.

Human spinal balance assessment relies considerably on sagittal radiographic parameter measurement. Deep learning could be applied for automatic landmark detection and alignment analysis, with mild to moderate standard errors and favourable correlations with manual measurement. In this study, based on 2210 annotated images of various spinal disease aetiologies, we developed deep learning models capable of automatically locating 45 anatomic landmarks and subsequently generating 18 radiographic parameters on a whole-spine lateral radiograph. In the assessment of model performance, the localisation accuracy and learning speed were the highest for landmarks in the cervical area, followed by those in the lumbosacral, thoracic, and femoral areas. All the predicted radiographic parameters were significantly correlated with ground truth values (all p < 0.001). The human and artificial intelligence comparison revealed that the deep learning model was capable of matching the reliability of doctors for 15/18 of the parameters. The proposed automatic alignment analysis system was able to localise spinal anatomic landmarks with high accuracy and to generate various radiographic parameters with favourable correlations with manual measurements.

Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs.

Spinal tumors represent 15% of all central nervous system malignancies, with intramedullary spinal cord tumors (IMSCTs) being rare. Predominantly ependymomas and astrocytomas, IMSCTs often present late, leading to significant morbidity and mortality. Surgical excision is key but challenging due to the tumors' complex, invasive nature. Treatment involves a multidisciplinary approach, considering tumor type and patient condition, ranging from subtotal to gross total resection, possibly with adjuvant therapy. This study uses machine learning on National Cancer Database data to predict postoperative outcomes, aiming to develop a risk calculator for clinicians to assess mortality and extended hospital stay risks post-surgery.

Article	Year
Characterization of a novel caudal vertebral interbody fusion in a rat tail model: An implication for future material and mechanical testing. Biomedical journal\| PubMed ID: 28411885	2017
Is Convex Derotation Equally Effective as Concave Derotation for Achieving Adequate Correction of Selective Lenke's Type- 1 Scoliosis? Indian journal of orthopaedics\| PubMed ID: 30078893	2018
Artificial Intelligence for Automatic Measurement of Sagittal Vertical Axis Using ResUNet Framework. Journal of clinical medicine\| PubMed ID: 31683913	2019
Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs. Scientific reports\| PubMed ID: 33828159	2021
Machine learning-driven national analysis for predicting adverse outcomes in intramedullary spinal cord tumor surgery. European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society\| PubMed ID: 40555868	2025

Yu-Cheng Yeh

Biography

JoVE Journal Publications

Other Publications