Fracture Type Identification Using Extra Tree Classifier

2021 ◽  
Author(s):  
Rocky Upadhyay ◽  
Prakash Singh Tanwar ◽  
Sheshang Degadwala
Keyword(s):  
Fracture Type ◽  
Tree Classifier

Single Hemicerclage for Lateral Type B Malleolar Fracture - A Novel, Minimal and Reliable Osteosynthesis

Swiss Surgery ◽  
2003 ◽  
Vol 9 (6) ◽  
pp. 283-288
Author(s):  
Maurer ◽  
Stamenic ◽  
Stouthandel ◽  
Ackermann ◽  
Gonzenbach
Keyword(s):  
Ankle Joint ◽  
Weight Bearing ◽  
Fracture Type ◽  
Type B ◽  
Malleolar Fracture ◽  
Metallic Wire ◽  
Long Term Outcome ◽  
X Ray ◽  
Upper Ankle Joint ◽  
Upper Ankle

Aim of study: To investigate the short- and long-term outcome of patients with isolated lateral malleolar fracture type B treated with a single hemicerclage out of metallic wire or PDS cord. Methods: Over an 8-year period 97 patients were treated with a single hemicerclage for lateral malleolar fracture type B and 89 were amenable to a follow-up after mean 39 months, including interview, clinical examination and X-ray controls. Results: The median operation time was 35 minutes (range 15-85 min). X-ray controls within the first two postoperative days revealed an anatomical restoration of the upper ankle joint in all but one patient. The complication rate was 8%: hematoma (2 patients), wound infection (2), Sudeck's dystrophy (2) and deep vein thrombosis (1). Full weight-bearing was tolerated at median 6.0 weeks (range 2-26 weeks). No secondary displacement, delayed union or consecutive arthrosis of the upper ankle joint was observed. All but one patient had restored symmetric joint mobility. Ninety-seven percent of patients were satisfied or very satisfied with the outcome. Following bone healing, hemicerclage removal was necessary in 19% of osteosyntheses with metallic wire and in none with PDS cord. Conclusion: The single hemicerclage is a novel, simple and reliable osteosynthesis technique for isolated lateral type B malleolar fractures and may be considered as an alternative to the osteosynthesis procedures currently in use.

scholarly journals A machine learning approach for accurate and real-time DNA sequence identification

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yiren Wang ◽  
Mashari Alangari ◽  
Joshua Hihath ◽  
Arindam K. Das ◽  
M. P. Anantram
Keyword(s):  
Real Time ◽  
Dna Sequence ◽  
Single Molecule ◽  
Identification Accuracy ◽  
Measuring System ◽  
Identification System ◽  
Genetic Sequencing ◽  
Sequence Identification ◽  
Tree Classifier ◽  
Single Mismatch

Abstract Background The all-electronic Single Molecule Break Junction (SMBJ) method is an emerging alternative to traditional polymerase chain reaction (PCR) techniques for genetic sequencing and identification. Existing work indicates that the current spectra recorded from SMBJ experimentations contain unique signatures to identify known sequences from a dataset. However, the spectra are typically extremely noisy due to the stochastic and complex interactions between the substrate, sample, environment, and the measuring system, necessitating hundreds or thousands of experimentations to obtain reliable and accurate results. Results This article presents a DNA sequence identification system based on the current spectra of ten short strand sequences, including a pair that differs by a single mismatch. By employing a gradient boosted tree classifier model trained on conductance histograms, we demonstrate that extremely high accuracy, ranging from approximately 96 % for molecules differing by a single mismatch to 99.5 % otherwise, is possible. Further, such accuracy metrics are achievable in near real-time with just twenty or thirty SMBJ measurements instead of hundreds or thousands. We also demonstrate that a tandem classifier architecture, where the first stage is a multiclass classifier and the second stage is a binary classifier, can be employed to boost the single mismatched pair’s identification accuracy to 99.5 %. Conclusions A monolithic classifier, or more generally, a multistage classifier with model specific parameters that depend on experimental current spectra can be used to successfully identify DNA strands.

scholarly journals Epidemiology and aetiology of male and female sexual dysfunctions related to pelvic ring injuries: a systematic review

2021 ◽  
Author(s):  
Giuseppe Rovere ◽  
Andrea Perna ◽  
Luigi Meccariello ◽  
Domenico De Mauro ◽  
Alessandro Smimmo ◽  
...  
Keyword(s):  
Sexual Dysfunction ◽  
Sexual Function ◽  
Pelvic Fracture ◽  
Pelvic Ring ◽  
Pelvic Fractures ◽  
Sexual Dysfunctions ◽  
Fracture Type ◽  
Ring Fracture ◽  
Male And Female ◽  
Pelvic Ring Injuries

Abstract Introduction Pelvic ring injuries, frequently caused by high energy trauma, are associated with high rates of morbidity and mortality (5–33%), often due to significant blood loss and disruption of the lumbosacral plexus, genitourinary system, and gastrointestinal system. The aim of the present study is to perform a systematic literature review on male and female sexual dysfunctions related to traumatic lesions of the pelvic ring. Methods Scopus, Cochrane Library MEDLINE via PubMed, and Embase were searched using the keywords: “Pelvic fracture,” “Pelvic Ring Fracture,” “Pelvic Ring Trauma,” “Pelvic Ring injury,” “Sexual dysfunction,” “Erectile dysfunction,” “dyspareunia,” and their MeSH terms in any possible combination. The following questions were formulated according to the PICO (population (P), intervention (I), comparison (C), and outcome (O)) scheme: Do patients suffering from pelvic fracture (P) report worse clinical outcomes (C), in terms of sexual function (O), when urological injury occurs (I)? Is the sexual function (O) influenced by the type of fracture (I)? Results After screening 268 articles by title and abstract, 77 were considered eligible for the full-text analysis. Finally 17 studies that met inclusion criteria were included in the review. Overall, 1364 patients (902 males and 462 females, M/F ratio: 1.9) suffering from pelvic fractures were collected. Discussion Pelvic fractures represent challenging entities, often concomitant with systemic injuries and subsequent morbidity. Anatomical consideration, etiology, correlation between sexual dysfunction and genitourinary lesions, or pelvic fracture type were investigated. Conclusion There are evidences in the literature that the gravity and frequency of SD are related with the pelvic ring fracture type. In fact, patients with APC, VS (according Young-Burgess), or C (according Tile) fracture pattern reported higher incidence and gravity of SD. Only a week association could be found between GUI and incidence and gravity of SD, and relationship between surgical treatment and SD. Electrophysiological tests should be routinely used in patient suffering from SD after pelvic ring injuries.

scholarly journals AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3830
Author(s):  
Ahmad Almadhor ◽  
Hafiz Tayyab Rauf ◽  
Muhammad Ikram Ullah Lali ◽  
Robertas Damaševičius ◽  
Bader Alouffi ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Color Difference ◽  
Plant Diseases ◽  
Economic Losses ◽  
Production Loss ◽  
Postharvest Diseases ◽  
Tropical Regions ◽  
Guava Fruit ◽  
Tree Classifier

Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics’ apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers’ improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants’ leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions.

scholarly journals A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 169
Author(s):  
Sergi Gómez-Quintana ◽  
Christoph E. Schwarz ◽  
Ihor Shelevytsky ◽  
Victoriya Shelevytska ◽  
Oksana Semenova ◽  
...  
Keyword(s):  
Patent Ductus Arteriosus ◽  
Heart Defects ◽  
Ductus Arteriosus ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Heart Sounds ◽  
Decision Tree Classifier ◽  
Support Tool ◽  
Tree Classifier ◽  
Patent Ductus

The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

scholarly journals Dislocated hinge fractures are associated with malunion after lateral closing wedge distal femoral osteotomy

2021 ◽  
Author(s):  
Marco-Christopher Rupp ◽  
Philipp W. Winkler ◽  
Patricia M. Lutz ◽  
Markus Irger ◽  
Philipp Forkel ◽  
...  
Keyword(s):  
Femoral Osteotomy ◽  
Common Finding ◽  
Fracture Type ◽  
Varus Malalignment ◽  
Distal Femoral Osteotomy ◽  
Closing Wedge ◽  
Increased Risk ◽  
Surgical Data ◽  
Technical Factors ◽  
Hinge Fracture

Abstract Purpose To evaluate the incidence, morphology, and associated complications of medial cortical hinge fractures after lateral closing wedge distal femoral osteotomy (LCW-DFO) for varus malalignment and to identify constitutional and technical factors predisposing for hinge fracture and consecutive complications. Methods Seventy-nine consecutive patients with a mean age of 47 ± 12 years who underwent LCW-DFO for symptomatic varus malalignment at the authors’ institution between 01/2007 and 03/2018 with a minimum of 2-year postoperative time interval were enrolled in this retrospective observational study. Demographic and surgical data were collected. Measurements evaluating the osteotomy cut (length, wedge height, hinge angle) and the location of the hinge (craniocaudal and mediolateral orientation, relation to the adductor tubercle) were conducted on postoperative anterior–posterior knee radiographs and the incidence and morphology of medial cortical hinge fractures was assessed. A risk factor analysis of constitutional and technical factors predisposing for the incidence of a medial cortical hinge fracture and consecutive complications was conducted. Results The incidence of medial cortical hinge fractures was 48%. The most frequent morphological type was an extension fracture type (68%), followed by a proximal (21%) and distal fracture type (11%). An increased length of the osteotomy in mm (53.1 ± 10.9 vs. 57.7 ± 9.6; p = 0.049), an increased height of the excised wedge in mm (6.5 ± 1.9 vs. 7.9 ± 3; p = 0.040) as well as a hinge location in the medial sector of an established sector grid (p = 0.049) were shown to significantly predispose for the incidence of a medial cortical hinge fracture. The incidence of malunion after hinge fracture (14%) was significantly increased after mediolateral dislocation of the medial cortical bone > 2 mm (p < 0.05). Conclusion Medial cortical hinge fractures after LCW-DFO are a common finding. An increased risk of sustaining a hinge fracture has to be expected with increasing osteotomy wedge height and a hinge position close to the medial cortex. Furthermore, dislocation of a medial hinge fracture > 2 mm was associated with malunion and should, therefore, be avoided. Level of evidence Prognostic study; Level IV.

scholarly journals Effect of Osteoporosis on Proximal Humerus Fractures

2020 ◽  
Vol 11 ◽  
pp. 215145932098539
Author(s):  
Anil Taskesen ◽  
Ali Göçer ◽  
Kadir Uzel ◽  
Yüksel Uğur Yaradılmış
Keyword(s):  
Surgical Outcomes ◽  
Proximal Humerus ◽  
City Hospital ◽  
Proximal Humerus Fractures ◽  
Fracture Type ◽  
Age And Gender ◽  
Humerus Fractures ◽  
The Mean ◽  
And Gender ◽  
Fracture Types

Introduction: Proximal humerus fractures (PHF) constitute the majority of the most common osteoporotic fractures. Bone density measurements can affect treatment methods and outcomes. This study was aimed to investigate the effect of osteoporosis values, measured from direct radiographs, on fracture type, surgical outcomes. Methods: 248 patients over 50 years of age who presented to Mersin City Hospital between 2017 and 2020 with proximal humeral fractures were retrospectively evaluated. The age and gender of the patients and the fracture types were evaluated according to the AO classification system from the direct radiographs obtained at the time of admission were recorded. The Tingart cortical thickness and deltoid tuberosity index (DTI) measurements were used to assess osteoporosis status in all patients. Postoperative and follow-up radiographs of 45 patients, treated with fixed-angle proximal humeral locking plate, were evaluated for radiographic results and their correlations with osteoporosis measurements were examined. Results: According to the demographic characteristics of the patients, 171 patients were female and 77 patients were male (F/M: 3/1), and mean age was 69.2 ± 11.66 (50-95). Considering the bone quality parameters in all patients, the mean Tingart value was 5.8 ± 1.6 mm and the mean DTI was 1.43 ± 0.17, where there was a correlation between the Tingart value and DTI (r = 0.810 and p < 0.001). Although there was a statistically significant relationship between the osteoporosis parameters and age and gender (p < 0.001 and p = 0.023, respectively), main AO fracture types were not related to osteoporosis (p < 0.05). In the operated group (n = 48, 19%), 19 patients (42%) showed poor outcomes, which were not associated with age and osteoporosis parameters. Conclusion: This study was concluded that osteoporosis parameters differ between genders and age groups in patients with PHF, however osteoporosis is not the main factor affecting the fracture type and surgical outcomes.

scholarly journals Optimization of Physical Activity Recognition for Real-Time Wearable Systems: Effect of Window Length, Sampling Frequency and Number of Features

2019 ◽  
Vol 9 (22) ◽  
pp. 4833 ◽  
Author(s):  
Ardo Allik ◽  
Kristjan Pilt ◽  
Deniss Karai ◽  
Ivo Fridolin ◽  
Mairo Leier ◽  
...  
Keyword(s):  
Physical Activity ◽  
Feature Selection ◽  
Real Time ◽  
Sampling Frequency ◽  
Classification Performance ◽  
Window Length ◽  
Decision Tree Classifier ◽  
Acceleration Signal ◽  
Wearable Systems ◽  
Tree Classifier

The aim of this study was to develop an optimized physical activity classifier for real-time wearable systems with the focus on reducing the requirements on device power consumption and memory buffer. Classification parameters evaluated in this study were the sampling frequency of the acceleration signal, window length of the classification fragment, and the number of classification features, found with different feature selection methods. For parameter evaluation, a decision tree classifier was created based on the acceleration signals recorded during tests, where 25 healthy test subjects performed various physical activities. Overall average F1-score achieved in this study was about 0.90. Similar F1-scores were achieved with the evaluated window lengths of 5 s (0.92 ± 0.02) and 3 s (0.91 ± 0.02), while classification performance with 1 s were lower (0.87 ± 0.02). Tested sampling frequencies of 50 Hz, 25 Hz, and 13 Hz had similar results with most classified activity types, with an exception of outdoor cycling, where differences were significant. Using forward sequential feature selection enabled the decreasing of the number of features from initial 110 features to about 12 features without lowering the classification performance. The results of this study have been used for developing more efficient real-time physical activity classifiers.

Sign in / Sign up

Export Citation Format

Share Document