scholarly journals Applying Machine Learning Methods Toward Classification Based on Small Datasets: Application to Shoulder Labral Tears

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Daniel R. Clymer ◽  
Jason Long ◽  
Carmen Latona ◽  
Sam Akhavan ◽  
Philip LeDuc ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Medical Imaging ◽  
Imaging Techniques ◽  
Mr Arthrography ◽  
Mathematical Optimization ◽  
Pattern Search ◽  
Machine Learning Techniques ◽  
Labral Tears ◽  
Number Of Patients

Abstract Machine learning is a powerful tool that can be applied to pattern search and mathematical optimization for making predictions on new data with unknown labels. In the field of medical imaging, one challenge with applying machine learning techniques is the limited size and relative expense of obtaining labeled data. For example, in glenoid labral tears, current imaging diagnosis is best achieved by imaging through magnetic resonance (MR) arthrography, a method of injecting contrast-enhancing material into the joint that can potentially cause discomfort to the patient, and adds expense compared to a standard magnetic resonance image (MRI). This work proposes limiting the use of MR arthrography through a medical diagnostic approach, based on convolutional neural networks (CNNs) and transfer learning from a separate medical imaging dataset to improve the efficiency and effectiveness. The results indicate an effective method applied to a small dataset of unenhanced shoulder MRI in order to diagnose labral tear severity while potentially significantly reducing cost and reducing unnecessary invasive imaging techniques. The proposed method ultimately can reduce physician workload while ensuring that the least number of patients as possible need to be subjected to an additional invasive contrast-enhanced imaging procedure.

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

Development of a method for in vivo mechanical characterization of articular cartilage

2017 ◽  
Author(s):  
◽  
Joe Rexwinkle
Keyword(s):  
Machine Learning ◽  
Hospital Setting ◽  
The United States ◽  
Machine Learning Techniques ◽  
Mechanical State ◽  
University Of Missouri ◽  
Orthopaedic Research ◽  
Early Results ◽  
Number Of Patients

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Arthritis is one of the leading causes of disability in the United States and the second most expensive to treat according to the CDC. One of the key difficulties in diagnosing and treating arthritis, in particular osteoarthritis, is that the mechanisms for progression of the disease are poorly characterized. Mechanical engineer Joe Rexwinkle, working with Dr. Ferris Pfeiffer and the Thompson Lab for Regenerative Orthopaedics, aimed to shed some light on the links between cartilage biology and the degradation seen in osteoarthritis. The study began with obtaining cartilage samples from six patients undergoing total knee replacements and collecting information on several biomarkers with known relevance to osteoarthritis. Specifically, the concentrations of several proteins which may be determined in a standard hospital lab were analyzed. The samples were then tested to determine their mechanical properties, since the progression of osteoarthritis is always accompanied by the physical degradation of the tissue. Machine learning techniques, which are gaining increasing popularity in the field of orthopaedic research, were then used to model the relationships between these biomarkers and the mechanical state of the tissue. These models were found to be highly accurate in characterizing the mechanical state of the tissue, even when limited only to the protein concentrations that one could find in a standard hospital lab. This study has not yet produced a tool which may be used in a hospital setting, considering the low number of patients included in this study, but it does reveal promising early results in using machine learning to characterize osteoarthritis, a task which has thus far eluded the orthopaedic research community.

scholarly journals Imaging of the Shoulder Bankart Lesion and its Variants

2019 ◽  
Vol 9 (1) ◽  
pp. 33-39
Author(s):  
Kheng Song Leow ◽  
Soo Fin Low ◽  
Wilfred CG Peh
Keyword(s):  
Imaging Modality ◽  
Imaging Techniques ◽  
Mr Arthrography ◽  
Recurrent Dislocation ◽  
Tissue Structure ◽  
Bankart Lesion ◽  
Glenoid Labrum ◽  
Labral Tears ◽  
Chronic Shoulder Instability ◽  
Labral Injury

The glenoid labrum is an important soft tissue structure that provides stability to the shoulder joint. When the labrum is injured, affected patients may present with chronic shoulder instability and future recurrent dislocation. The Bankart lesion is the most common labral injury, and is often accompanied by a Hill-Sachs lesion of the humerus. Various imaging techniques are available for detection of the Bankart lesion and its variants, such as anterior labroligamentous periosteal sleeve avulsion and Perthes lesion. Direct magnetic resonance (MR) arthrography is currently the imaging modality of choice for evaluation of the various types of labral tears. As normal anatomical variants of glenoid labrum are not uncommonly encountered, familiarity with appearances of this potential pitfall helps avoid misdiagnosis.

scholarly journals Robust Feature Engineering for Parkinson Disease Diagnosis: New Machine Learning Techniques (Preprint)

2019 ◽  
Author(s):  
Max Wang ◽  
Wenbo Ge ◽  
Deborah Apthorp ◽  
Hanna Suominen
Keyword(s):  
Machine Learning ◽  
Parkinson Disease ◽  
Neurodegenerative Disorder ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Feature Engineering ◽  
Data Set ◽  
Performance Improvements ◽  
Number Of Patients

BACKGROUND Parkinson disease (PD) is a common neurodegenerative disorder that affects between 7 and 10 million people worldwide. No objective test for PD currently exists, and studies suggest misdiagnosis rates of up to 34%. Machine learning (ML) presents an opportunity to improve diagnosis; however, the size and nature of data sets make it difficult to generalize the performance of ML models to real-world applications. OBJECTIVE This study aims to consolidate prior work and introduce new techniques in feature engineering and ML for diagnosis based on vowel phonation. Additional features and ML techniques were introduced, showing major performance improvements on the large mPower vocal phonation data set. METHODS We used 1600 randomly selected /aa/ phonation samples from the entire data set to derive rules for filtering out faulty samples from the data set. The application of these rules, along with a joint age-gender balancing filter, results in a data set of 511 PD patients and 511 controls. We calculated features on a 1.5-second window of audio, beginning at the 1-second mark, for a support vector machine. This was evaluated with 10-fold cross-validation (CV), with stratification for balancing the number of patients and controls for each CV fold. RESULTS We showed that the features used in prior literature do not perform well when extrapolated to the much larger mPower data set. Owing to the natural variation in speech, the separation of patients and controls is not as simple as previously believed. We presented significant performance improvements using additional novel features (with 88.6% certainty, derived from a Bayesian correlated t test) in separating patients and controls, with accuracy exceeding 58%. CONCLUSIONS The results are promising, showing the potential for ML in detecting symptoms imperceptible to a neurologist.

Imaging in carpal instability

2015 ◽  
Vol 41 (1) ◽  
pp. 22-34 ◽  
Author(s):  
N. K. Ramamurthy ◽  
A. J. Chojnowski ◽  
A. P. Toms
Keyword(s):  
Magnetic Resonance ◽  
Computerized Tomography ◽  
Dynamic Instability ◽  
Imaging Techniques ◽  
Mr Arthrography ◽  
Plain Film ◽  
Carpal Instability ◽  
Accurate Identification ◽  
Cross Sectional ◽  
Plain Film Radiography

Carpal instability is a complex and heterogeneous clinical condition. Management requires accurate identification of structural injury with an understanding of the resultant movement (kinematic) and load transfer (kinetic) failure. Static imaging techniques, such as plain film radiography, stress views, ultrasound, magnetic resonance, MR arthrography and computerized tomography arthrography, may accurately depict major wrist ligamentous injury. Dynamic ultrasound and videofluoroscopy may demonstrate dynamic instability and kinematic dysfunction. There is a growing evidence base for the diagnostic accuracy of these techniques in detecting intrinsic ligament tears, but there are limitations. Evidence of their efficacy and relevance in detection of non-dissociative carpal instability and extrinsic ligament tears is weak. Further research into the accuracy of existing imaging modalities is still required. Novel techniques, including four-dimensional computerized tomography and magnetic resonance, can evaluate both cross-sectional and functional carpal anatomy. This is a narrative review of level-III studies evaluating the role of imaging in carpal instability.

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