Editorial comment to artificial intelligence X-ray measurement technology of anatomical parameters related to lumbosacral stability

AbstractWe apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both ‘on-the-fly’ and during post hoc analysis.

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Morphological consistency of bilateral hip joints in adults based on the X-ray and CT data

Surgical and Radiologic Anatomy ◽

10.1007/s00276-020-02676-4 ◽

2021 ◽

Author(s):

Ran Zhao ◽

Hong Cai ◽

Hua Tian ◽

Ke Zhang

Keyword(s):

Hip Joint ◽

Morphological Difference ◽

Medullary Cavity ◽

Size Difference ◽

Single Plane ◽

X Ray ◽

Stem Size ◽

The Difference ◽

Ct Data ◽

Anatomical Parameters

Abstract Purpose The application of the anatomical parameters of the contralateral hip joint to guide the preoperative template of the affected side relies on the bilateral hip symmetry. We investigated the bilateral hip symmetry and range of anatomical variations by measurement and comparison of bilateral hip anatomical parameters. Methods This study included 224 patients (448 hips) who were diagnosed with osteoarthritis (OA) and avascular necrosis (AVN) of the femur head, and underwent bilateral primary total hip arthroplasty (THA) in our hospital from January 2012 to August 2020. Imaging data included 224 patients X-ray and 30 CT data at the end of the cohort. Anatomical parameters, including the acetabular abduction angle and trochanteric height, were measured using the Noble method. Postoperative measurements included stem size, difference of leg length and offset. Results Except for the isthmus width, there were no significant differences in the anatomical morphology of the hip joint. Among the demographic factors, there was a correlation between body weight and NSA. Among various anatomical parameters, a correlation was present between medullary cavity widths of T + 20, T, and T − 20. The difference in the use of stem size is not due to the morphological difference of bilateral medullary cavity, but due to the different of 1- or 2-stage surgery. Conclusion Bilateral symmetry was present among the patients with normal morphology of the hip medullary cavity, theoretically confirming the feasibility of structural reconstruction of the hip joint using the hip joint on the uninjured side. Additionally, the difference in the morphology of the hip medullary cavity is not present in a single plane but is synergistically affected by multiple adjacent planes.

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Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

Health and Technology ◽

10.1007/s12553-021-00520-2 ◽

2021 ◽

Vol 11 (2) ◽

pp. 411-424 ◽

Cited By ~ 1

Author(s):

José Daniel López-Cabrera ◽

Rubén Orozco-Morales ◽

Jorge Armando Portal-Diaz ◽

Orlando Lovelle-Enríquez ◽

Marlén Pérez-Díaz

Keyword(s):

Artificial Intelligence ◽

X Ray ◽

X Ray Imaging ◽

Chest X Ray

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Medical Physics Contributes to The Advancement in Medicine

ASM Science Journal ◽

10.32802/asmscj.2020.505 ◽

2021 ◽

Vol 14 ◽

pp. 1-7

Author(s):

Kwan Hoong Ng ◽

Jeannie Hsiu Ding Wong ◽

Chai Hong Yeong ◽

Hafiz Mohd Zin ◽

Noriah Jamal

Keyword(s):

Artificial Intelligence ◽

Nuclear Medicine ◽

Big Data ◽

New Technologies ◽

Medical Physics ◽

Knowledge And Skills ◽

X Ray ◽

Medical Physicists ◽

Medicine Today ◽

Improve Health

Medical physics is the application of physics principles and techniques in medicine. Medical physicists are actively applying their knowledge and skills in the prevention, diagnosis and treatment of diseases to improve health via research and clinical practice. In this paper, we present the roles of medical physicists in the three primary fields, namely, diagnostic imaging, radiotherapy and nuclear medicine. Medical physicists have been playing a crucial role in the advancement of new technologies that have revolutionised medicine today. This includes the continuous development of medical imaging and radiotherapy techniques since the discovery of X-ray and radioactivity. The last decade has seen tremendous development in the field that allows for better diagnosis and targeted treatment of various diseases. In the era of big data and artificial intelligence, while medical physicists continue to ensure that the application of the technologies in medicine is optimal and safe, it is paramount for the profession to evolve and be equipped with new skills to continue to contribute to the advancement of medicine.

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Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

10.21203/rs.3.rs-66836/v2 ◽

2021 ◽

Author(s):

Ali Mohammad Alqudah ◽

Shoroq Qazan ◽

Ihssan S. Masad

Keyword(s):

Artificial Intelligence ◽

Input Image ◽

Image Size ◽

X Ray ◽

Softmax Classifier ◽

Non Invasive ◽

Efficient Detection ◽

X Ray Imaging ◽

Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

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Current limitations to identify covid-19 using artificial intelligence with chest x-ray imaging (part ii). The shortcut learning problem

Health and Technology ◽

10.1007/s12553-021-00609-8 ◽

2021 ◽

Author(s):

José Daniel López-Cabrera ◽

Rubén Orozco-Morales ◽

Jorge Armando Portal-Díaz ◽

Orlando Lovelle-Enríquez ◽

Marlén Pérez-Díaz

Keyword(s):

Artificial Intelligence ◽

Learning Problem ◽

X Ray ◽

X Ray Imaging ◽

Chest X Ray

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The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

Diagnostics ◽

10.3390/diagnostics11122206 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2206

Author(s):

Dana Li ◽

Lea Marie Pehrson ◽

Carsten Ammitzbøl Lauridsen ◽

Lea Tøttrup ◽

Marco Fraccaro ◽

...

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Assessment Tool ◽

Cochrane Library ◽

Original Research ◽

Quality Assessment Tool ◽

Bias Assessment ◽

X Ray ◽

Chest X Ray ◽

Sensitivity Specificity

Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.

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New Standard Reference Materials for X-Ray Powder Diffraction

Advances in X-ray Analysis ◽

10.1154/s037603080001226x ◽

1982 ◽

Vol 26 ◽

pp. 45-51

Author(s):

Camden R. Hubbard

Keyword(s):

Reference Materials ◽

Nuclear Power ◽

Standard Reference Materials ◽

Measurement Technology ◽

National Bureau ◽

State Governments ◽

X Ray ◽

Ferrous Metals ◽

Research Organizations ◽

Improve Measurement

Standard Reference Materials (SRMs) from the National Bureau of Standards are samples or artifacts certified for one or more particular parameters. The NBS has produced SRHs since 1905 to aid commerce, to improve measurement technology and to assist in the enforcement of regulations. Today nearly 900 different SRHs are available to serve major segments of industry such as ferrous metals, nonferrous metals, mining, glass, primary chemicals, computer, nuclear power and electronics. In addition to the industrial customers, major SRM users include both federal and state governments, universities and nonprofit research organizations.

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