A Machine Learning Algorithm to Predict the Probability of (Occult) Posterior Malleolar Fractures Associated With Tibial Shaft Fractures to Guide “Malleolus First” Fixation

2020 ◽  
Vol 34 (3) ◽  
pp. 131-138 ◽  
Author(s):  
Laurent A. M. Hendrickx ◽  
Garret L. Sobol ◽  
David W. G. Langerhuizen ◽  
Anne Eva J. Bulstra ◽  
Jeremy Hreha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tibial Shaft ◽  
Malleolar Fractures ◽  
Shaft Fractures ◽  
Tibial Shaft Fractures

Incidence, Predictors, and Fracture Mapping of (Occult) Posterior Malleolar Fractures Associated With Tibial Shaft Fractures

2019 ◽  
Vol 33 (12) ◽  
pp. e452-e458 ◽  
Author(s):  
Laurent A.M. Hendrickx ◽  
Megan E. Cain ◽  
Inger N. Sierevelt ◽  
Bhavin Jadav ◽  
Gino M.M.J. Kerkhoffs ◽  
...  
Keyword(s):  
Tibial Shaft ◽  
Malleolar Fractures ◽  
Shaft Fractures ◽  
Fracture Mapping ◽  
Tibial Shaft Fractures

Posterior Malleolar Fractures Associated With Tibial Shaft Fractures and Sequence of Fixation

2016 ◽  
Vol 30 (10) ◽  
pp. 568-571 ◽  
Author(s):  
Harish Kempegowda ◽  
Hemil H. Maniar ◽  
Raveesh Richard ◽  
Akhil Tawari ◽  
Graham Jove ◽  
...  
Keyword(s):  
Tibial Shaft ◽  
Malleolar Fractures ◽  
Shaft Fractures ◽  
Tibial Shaft Fractures

scholarly journals Incidence and missed diagnosis risk of occult posterior malleolar fractures associated with the tibial shaft fractures: a systematic review

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Zhongzheng Wang ◽  
Wei Chen ◽  
Yanbin Zhu ◽  
Siyu Tian ◽  
Kuo Zhao ◽  
...  
Keyword(s):  
Fragment Size ◽  
Treatment Strategies ◽  
Tibial Shaft ◽  
X Ray ◽  
Malleolar Fractures ◽  
Combined Injury ◽  
Shaft Fractures ◽  
Diagnosis Rate ◽  
Missed Diagnosis ◽  
Tibial Shaft Fractures

Abstract Background Tibial shaft fractures (TSFs) combined with occult posterior malleolar fractures (PMFs) are becoming widely recognized in the field of orthopedics. The purpose of this study was to determine the clinical incidence, missed diagnosis rate, and treatment strategies of this combined injury. Methods PubMed, Cochrane, and MEDLINE Ovid databases were searched for articles of English language from 1988 to 2020, identifying 1549 papers. Results Twenty-one of the 1278 identified studies were eligible for inclusion. Each study reported on the incidence of this combined injury, and 12 studies documented the missed diagnosis rate. Seventeen studies reported surgical intervention strategies for PMFs. In the present review, PMFs frequently occurred in spiral TSFs (70%), especially distal third spiral TSFs (70.4%), based on CT scans or additional MRI. Based on the original X-ray detection, approximately 50% of PMFs were missed in patients with a combined injury. In addition, the treatment strategies for PMFs were inconsistent. Most studies (11/17) believe that specific surgical management needs to be developed based on the fragment size, displacement, and stability of the PMF. Conclusions For patients with TSFs, spiral TSFs, especially distal third spiral TSFs, are closely related to PMFs and are often not sufficiently diagnosed by X-ray alone. Advanced CT and MRI examinations can significantly reduce the missed diagnosis rate of occult PMFs. According to available literature, the treatment strategy for PMFs associated with TSFs is questionable without convincing evidence of benefit.

Results of a Computed Tomography Protocol Evaluating Distal Third Tibial Shaft Fractures to Assess Noncontiguous Malleolar Fractures

2011 ◽  
Vol 71 (1) ◽  
pp. 163-168 ◽  
Author(s):  
Gregory J. Purnell ◽  
Ericka R. Glass ◽  
Daniel T. Altman ◽  
Robert L. Sciulli ◽  
Matthew T. Muffly ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Tibial Shaft ◽  
Malleolar Fractures ◽  
Shaft Fractures ◽  
Tibial Shaft Fractures

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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