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Mohammadi, Farid Ghareh

https://orcid.org/0000-0002-5236-9059

A5011566382 | I2801572250

Mayo Clinic

Jacksonville College

Citations 9

Artificial Intelligence Impact on Burnout in Radiologists—Alleviation or Exacerbation?

JAMA Network Open | November 22, 2024 | Volume: 7 | Issue: 11 | Pages: e2448720

10.1001/jamanetworkopen.2024.48720 | PMID 39576648

Opportunistic Screening for Osteoporosis Using Hand Radiographs – A Feature Augmentation Study Technique (FAST)

Studies in Health Technology and Informatics | August 23, 2024 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.3233/shti240411 | PMID 39176740

Patients with low bone mineral density (BMD) face an increased risk of fractures, yet are frequently undiagnosed. Consequently, it is imperative to have opportunistically screen for low BMD in patients undergoing other medical evaluations. This retrospective study encompassed 422 patients aged ≥ 50 who underwent both dual-energy X-ray absorptiometry (DXA) and hand radiographs (modality of digital X-ray) from three different vendors within a 12-month period. The dataset was randomly divided into training/validation (n=338) and test (n=84) datasets. we sought to predict osteoporosis/osteopenia and establish correlations between bone textural analysis and DXA measurements. Our results demonstrate that the deep learning model achieved an accuracy of 77.38%, sensitivity of 77.38%, specificity of 73.63%, and an area under the curve (AUC) of 83% in detecting osteoporosis/osteopenia. These findings suggest that hand radiographs can serve as a viable screening tool for identifying individuals warranting formal DXA assessment for osteoporosis/osteopenia.
Steganalysis of Medical Radiographs for Radiographic Machine Identification

2023 International Conference on Computational Science and Computational Intelligence (CSCI) | July 19, 2024 | Volume: No volume available | Issue: No issue available | Pages: 1189-1196

10.1109/csci62032.2023.00194

DeepCT-SI: Deep Learning Algorithm for Computed Tomography Source Identification

2023 International Conference on Computational Science and Computational Intelligence (CSCI) | July 19, 2024 | Volume: No volume available | Issue: No issue available | Pages: 1117-1124

10.1109/csci62032.2023.00184

Region-Based Steganalysis of Medical Radiographs for Radiographic Machine Identification

2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) | April 9, 2024 | Volume: No volume available | Issue: No issue available | Pages: 1929-1935

10.1109/csce60160.2023.00318

Steganalysis of Medical Radiographs: a Deep Learning Approach Comparing the Importance of Using Content Pixels and Content-Free Pixels

2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) | April 9, 2024 | Volume: No volume available | Issue: No issue available | Pages: 202-209

10.1109/csce60160.2023.00037

Deep-RSIv2: An Efficient Content-Free Deep Learning Approach for Radiographs' Manufacturer and Model Identification

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) | February 12, 2024 | Volume: No volume available | Issue: No issue available | Pages: 1-8

10.1109/imcom60618.2024.10418243

Deep-RSI: Deep learning for Radiographs Source Identification

2022 International Conference on Computational Science and Computational Intelligence (CSCI) | August 25, 2023 | Volume: No volume available | Issue: No issue available | Pages: 1491-1496

10.1109/csci58124.2022.00264

Opportunistic Screening for Osteoporosis Using Hand Radiographs: A Preliminary Study

Studies in Health Technology and Informatics | May 19, 2023 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.3233/shti230306 | PMID 37203534

Patients with low bone mineral density (BMD) are at risk for fractures however are often undiagnosed. Therefore, there is a need to opportunistically screen for low BMD in patients who present for other studies. This is a retrospective study of 812 patients aged 50 years or older who had dual-energy X-ray absorptiometry (DXA) and radiographs of the hands within 12 months of each other. This dataset was randomly split into training/validation (n=533) and test (n=136) datasets. A deep learning (DL) framework was used to predict osteoporosis/osteopenia. Correlations between the textural analysis of the bones and DXA measurements were obtained. We found that the DL model had an accuracy of 82.00%, sensitivity of 87.03%, specificity of 61.00% and an area under the curve (AUC) of 74.00% to detect osteoporosis/osteopenia. Our findings show that radiographs of the hand can be used to screen for osteoporosis/osteopenia and identify patients who should get formal DXA evaluation.