scholarly journals Orbital exenteration — defect classification and reconstructive algorithm

Author(s):  
M.R. Kesting ◽  
S. Koerdt ◽  
N. Rommel ◽  
T. Muecke ◽  
K.D. Wolff ◽  
...  
2020 ◽  
Vol 36 (06) ◽  
pp. 696-702
Author(s):  
Nolan B. Seim ◽  
Enver Ozer ◽  
Sasha Valentin ◽  
Amit Agrawal ◽  
Mead VanPutten ◽  
...  

AbstractResection and reconstruction of midface involve complex ablative and reconstructive tools in head and oncology and maxillofacial prosthodontics. This region is extraordinarily important for long-term aesthetic and functional performance. From a reconstructive standpoint, this region has always been known to present challenges to a reconstructive surgeon due to the complex three-dimensional anatomy, the variable defects created, combination of the medical and dental functionalities, and the distance from reliable donor vessels for free tissue transfer. Another challenge one faces is the unique features of each individual resection defect as well as individual patient factors making each preoperative planning session and reconstruction unique. Understanding the long-term effects on speech, swallowing, and vision, one should routinely utilize a multidisciplinary approach to resection and reconstruction, including head and neck reconstructive surgeons, prosthodontists, speech language pathologists, oculoplastic surgeons, dentists, and/or craniofacial teams as indicated and with each practice pattern. With this in mind, we present our planning and reconstructive algorithm in midface reconstruction, including a dedicated focus on dental rehabilitation via custom presurgical planning.


Skull Base ◽  
2007 ◽  
Vol 16 (S 1) ◽  
Author(s):  
Pete Batra ◽  
Donald Lanza
Keyword(s):  

2021 ◽  
Vol 175 ◽  
pp. 114753
Author(s):  
Angel Gaspar Gonzalez-Rodriguez ◽  
Antonio Gonzalez-Rodriguez ◽  
Fernando Jose Castillo-Garcia

Author(s):  
David Blondheim

AbstractMachine learning (ML) is unlocking patterns and insight into data to provide financial value and knowledge for organizations. Use of machine learning in manufacturing environments is increasing, yet sometimes these applications fail to produce meaningful results. A critical review of how defects are classified is needed to appropriately apply machine learning in a production foundry and other manufacturing processes. Four elements associated with defect classification are proposed: Binary Acceptance Specifications, Stochastic Formation of Defects, Secondary Process Variation, and Visual Defect Inspection. These four elements create data space overlap, which influences the bias associated with training supervised machine learning algorithms. If this influence is significant enough, the predicted error of the model exceeds a critical error threshold (CET). There is no financial motivation to implement the ML model in the manufacturing environment if its error is greater than the CET. The goal is to bring awareness to these four elements, define the critical error threshold, and offer guidance and future study recommendations on data collection and machine learning that will increase the success of ML within manufacturing.


Author(s):  
Pan Tian ◽  
Chen Li ◽  
Hao Fu ◽  
Xueru Yu ◽  
Zhengying Wei ◽  
...  

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