Machine learning for proton path tracking in proton computed tomography

2021 ◽  
Author(s):  
Dimitrios Lazos ◽  
Charles-Antoine Collins-Fekete ◽  
Miroslaw Bober ◽  
Philip M Evans ◽  
Nikolaos Dikaios
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Path Tracking ◽  
Proton Computed Tomography

scholarly journals Improved contrast and noise of megavoltage computed tomography (MVCT) through cycle‐consistent generative machine learning

2020 ◽  
Author(s):  
Luciano Vinas ◽  
Jessica Scholey ◽  
Martina Descovich ◽  
Vasant Kearney ◽  
Atchar Sudhyadhom
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Megavoltage Computed Tomography

Radiology During the COVID-19 Pandemic: Mapping Radiology Literature in 2020

2021 ◽  
Vol 18 ◽  
Author(s):  
Nosaiba Al-Ryalat ◽  
Lna Malkawi ◽  
Ala'a Abu Salhiyeh ◽  
Faisal Abualteen ◽  
Ghaida Abdallah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Nuclear Medicine ◽  
Medical Imaging ◽  
Core Collection ◽  
Web Of Science ◽  
Vos Viewer ◽  
Literature Mapping ◽  
Link Strength ◽  
Linkage Strength

Objectives: Our aim was to assess articles published in the field of radiology, nuclear medicine, and medical imaging in 2020, analyzing the linkage of radiology-related topics with coronavirus disease 2019 (COVID-19) through literature mapping, along with a bibliometric analysis for publications. Methods: We performed a search on Web of Science Core Collection database for articles in the field of radiology, nuclear medicine, and medical imaging published in 2020. We analyzed the included articles using VOS viewer software, where we analyzed the co-occurrence of keywords, which represents major topics discussed. Of the resulting topics, literature map created, and linkage analysis done. Results: A total of 24,748 articles were published in the field of radiology, nuclear medicine, and medical imaging in 2020. We found a total of 61,267 keywords, only 78 keywords occurred more than 250 times. COVID-19 had 449 occurrences, 29 links, with a total link strength of 271. MRI was the topic most commonly appearing in 2020 radiology publications, while “computed tomography” has the highest linkage strength with COVID-19, with a linkage strength of 149, representing 54.98% of the total COVID-19 linkage strength, followed by “radiotherapy, and “deep and machine learning”. The top cited paper had a total of 1,687 citations. Nine out of the 10 most cited articles discussed COVID-19 and included “COVID-19” or “coronavirus” in their title, including the top cited paper. Conclusion: While MRI was the topic that dominated, CT had the highest linkage strength with COVID-19 and represent the topic of top cited articles in 2020 radiology publications.

scholarly journals Development and external validation of a deep learning-based computed tomography classification system for COVID-19

2021 ◽  
Author(s):  
Yuki KATAOKA
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Interstitial Pneumonia ◽  
External Validation ◽  
Usual Interstitial Pneumonia ◽  
Evaluation Process ◽  
High Sensitivity ◽  
Learning Model ◽  
Machine Learning Model ◽  
Ablation Study

Rationale: Currently available machine learning models for diagnosing COVID-19 based on computed tomography (CT) images are limited due to concerns regarding methodological flaws or underlying biases in the evaluation process. Objectives: We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR).Methods: We used 3128 images from a wide variety of two-gate data sources for the development and ablation study of the machine learning model. A total of 633 COVID-19 cases and 2295 non-COVID-19 cases were included in the study. We randomly divided cases into a development set and ablation set at a ratio of 8:2. For the ablation study, we used another dataset including 150 cases of interstitial pneumonia among non-COVID-19 images. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR.Result: In ablation study, using interstitial pneumonia images, the specificity of the model were 0.986 for usual interstitial pneumonia pattern, 0.820 for non-specific interstitial pneumonia pattern, 0.400 for organizing pneumonia pattern. In the external validation study, the sensitivity and specificity of the model were 0.869 and 0.432, respectively, at the low-level cutoff, and 0.724 and 0.721, respectively, at the high-level cutoff.Conclusions: Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner. Further studies are warranted to improve model specificity.

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