Endocrine Obitopathy: a Personalized Approach to Patient Сare

2021 ◽  
pp. 57-66
Author(s):  
O. Petrenko ◽  
O. Prusak
Keyword(s):  
Vision Loss ◽  
Intraoperative Complications ◽  
Maxillofacial Surgery ◽  
Orbital Decompression ◽  
Morphological Parameters ◽  
Computer Aided Manufacturing ◽  
Endocrine Orbitopathy ◽  
Computer Aided ◽  
Personalized Approach ◽  
The Impact

Background. This article highlights the main approaches to the treatment of patients with endocrine orbitopathy (EO) and current opportunities for planning and conducting personalized orbital decompression, including the use of Computer-Aided Design (CAD) / Computer-Aided Manufacturing (CAM) technologies. Optimization of treatment of this pathology which can lead to changes in appearance, inability to perform usual work, social isolation, and in the most difficult cases to vision loss is a topical issue of ophthalmology and maxillofacial surgery. The aim. To analyze current information on basic approaches to the treatment of patients with endocrine orbitopathy and the possibility of using computer technologies for planning and performing personalized orbital decompression. Results. The use of corticosteroids, radiation therapy, surgical treatment are the key methods used to manage patients with EO. The use of CAD/CAM technologies allows to plan surgery according to the morphological features of the orbit of each patient. This can potentially affect the outcome of decompression and reduction of exophthalmos, as well as intraoperative complications. When using standard approaches, the results of decompression may vary significantly due to the differences in morphological parameters of the orbits. This dictates the need for a personalized approach to the management of patients with EO. Due to the significant variability of morphological parameters of the orbits in the case of standard approaches, the results in different patients can differ significantly, which dictates the need for a personalized approach to the management of patients with EA. Conclusions. EUGOGO 2016 recommends a multidisciplinary step-by-step approach to the management of patients with EO, depending on its activity and severity and the impact on the patient’s quality of life. A personalized approach using computer simulations and the development of surgical navigation templates for surgical decompression of the orbit will ensure optimal functional and aesthetic treatment results. Keywords: endocrine orbitopathy, treatment, orbital decompression, ComputerAided Design (CAD) / Computer-Aided Manufacturing (CAM) technologies, personalized approach.

Integrated Computer-Aided Manufacturing (ICAM) Architecture. Part 3. Volume 7. MFG01 Glossary.

1983 ◽  
Author(s):  
R. Heine ◽  
R. Prewett ◽  
S. Coleman ◽  
L. Beebe ◽  
B. Davis
Keyword(s):  
Computer Aided Manufacturing ◽  
Computer Aided

Bone tissue engineering in the greater omentum with computer-aided design/computer-aided manufacturing scaffolds is enhanced by a periosteum transplant

2020 ◽  
Author(s):  
Hendrik Naujokat ◽  
Klaas Loger ◽  
Juliane Schulz ◽  
Yahya Açil ◽  
Jörg Wiltfang
Keyword(s):  
Bone Formation ◽  
Bone Tissue ◽  
Computer Aided Design ◽  
Bone Tissue Regeneration ◽  
Histological Evaluation ◽  
Greater Omentum ◽  
Collagen Membrane ◽  
Computer Aided ◽  
3D Printed ◽  
The Impact

Aim: This study aimed to evaluate two different vascularized bone flap scaffolds and the impact of two barrier membranes for the reconstruction of critical-size bone defects. Materials & methods: 3D-printed scaffolds of biodegradable calcium phosphate and bioinert titanium were loaded with rhBMP-2 bone marrow aspirate, wrapped by a collagen membrane or a periosteum transplant and implanted into the greater omentum of miniature pigs. Results: Histological evaluation demonstrated significant bone formation within the first 8 weeks in both scaffolds. The periosteum transplant led to enhanced bone formation and a homogenous distribution in the scaffolds. The omentum tissue grew out a robust vascular supply. Conclusion: Endocultivation using 3D-printed scaffolds in the greater omentum is a very promising approach in defect-specific bone tissue regeneration.

scholarly journals Clinical implementation of a 3D4K-exoscope (Orbeye) in microneurosurgery

2021 ◽  
Author(s):  
Judith Rösler ◽  
Stefan Georgiev ◽  
Anna L. Roethe ◽  
Denny Chakkalakal ◽  
Güliz Acker ◽  
...  
Keyword(s):  
User Satisfaction ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Intraoperative Complications ◽  
Rank Test ◽  
User Group ◽  
Clinical Implementation ◽  
Physical Strain ◽  
Conventional Microscope ◽  
The Impact

AbstractExoscopic surgery promises alleviation of physical strain, improved intraoperative visualization and facilitation of the clinical workflow. In this prospective observational study, we investigate the clinical usability of a novel 3D4K-exoscope in routine neurosurgical interventions. Questionnaires on the use of the exoscope were carried out. Exemplary cases were additionally video-documented. All participating neurosurgeons (n = 10) received initial device training. Changing to a conventional microscope was possible at all times. A linear mixed model was used to analyse the impact of time on the switchover rate. For further analysis, we dichotomized the surgeons in a frequent (n = 1) and an infrequent (n = 9) user group. A one-sample Wilcoxon signed rank test was used to evaluate, if the number of surgeries differed between the two groups. Thirty-nine operations were included. No intraoperative complications occurred. In 69.2% of the procedures, the surgeon switched to the conventional microscope. While during the first half of the study the conversion rate was 90%, it decreased to 52.6% in the second half (p = 0.003). The number of interventions between the frequent and the infrequent user group differed significantly (p = 0.007). Main reasons for switching to ocular-based surgery were impaired hand–eye coordination and poor depth perception. The exoscope investigated in this study can be easily integrated in established neurosurgical workflows. Surgical ergonomics improved compared to standard microsurgical setups. Excellent image quality and precise control of the camera added to overall user satisfaction. For experienced surgeons, the incentive to switch from ocular-based to exoscopic surgery greatly varies.

scholarly journals The Impact of Vision Loss on Allocentric Spatial Coding

2020 ◽  
Vol 14 ◽  
Author(s):  
Chiara Martolini ◽  
Giulia Cappagli ◽  
Antonella Luparia ◽  
Sabrina Signorini ◽  
Monica Gori
Keyword(s):  
Vision Loss ◽  
Spatial Coding ◽  
The Impact

scholarly journals Getting a grip on the resilience to blur: The impact of simulated vision loss on a visually guided combat sports interaction

2021 ◽  
pp. 101941
Author(s):  
Kai Krabben ◽  
David Mann ◽  
Arnt van Helden ◽  
Youri Kalisvaart ◽  
Daniel Fortin-Guichard ◽  
...  
Keyword(s):  
Vision Loss ◽  
Combat Sports ◽  
Visually Guided ◽  
The Impact

scholarly journals A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2764
Author(s):  
Xin Yu Liew ◽  
Nazia Hameed ◽  
Jeremie Clos
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Main Process ◽  
Learning Approaches ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Computer Aided ◽  
Conventional Methods ◽  
The Impact

A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. In this review paper, we reviewed the existing state-of-the-art machine learning approaches applied at each stage involving conventional methods and deep learning methods, the comparisons within methods, and we provide technical details with advantages and disadvantages. The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to analyse and improve the existing techniques used. Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.

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