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Article ; Online: FDA Review of Radiologic AI Algorithms: Process and Challenges.

Zhang, Kuan / Khosravi, Bardia / Vahdati, Sanaz / Erickson, Bradley J

2024 Volume 310, Issue 1, Page(s) e230242

Abstract: A Food and Drug Administration (FDA)-cleared artificial intelligence (AI) algorithm misdiagnosed a finding as an intracranial hemorrhage in a patient, who was finally diagnosed with an ischemic stroke. This scenario highlights a notable failure mode of ... ...

Abstract	A Food and Drug Administration (FDA)-cleared artificial intelligence (AI) algorithm misdiagnosed a finding as an intracranial hemorrhage in a patient, who was finally diagnosed with an ischemic stroke. This scenario highlights a notable failure mode of AI tools, emphasizing the importance of human-machine interaction. In this report, the authors summarize the review processes by the FDA for software as a medical device and the unique regulatory designs for radiologic AI/machine learning algorithms to ensure their safety in clinical practice. Then the challenges in maximizing the efficacy of these tools posed by their clinical implementation are discussed.
MeSH term(s)	United States ; Humans ; Artificial Intelligence ; United States Food and Drug Administration ; Algorithms ; Software ; Machine Learning
Language	English
Publishing date	2024-01-01
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	80324-8
ISSN	1527-1315 ; 0033-8419
ISSN (online)	1527-1315
ISSN	0033-8419
DOI	10.1148/radiol.230242
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Article ; Online: Artificial Intelligence in Radiology: Overview of Application Types, Design, and Challenges.

Moassefi, Mana / Faghani, Shahriar / Khosravi, Bardia / Rouzrokh, Pouria / Erickson, Bradley J

Seminars in roentgenology

2023 Volume 58, Issue 2, Page(s) 170–177

MeSH term(s)	Humans ; Artificial Intelligence ; Radiography ; Radiology
Language	English
Publishing date	2023-02-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	80310-8
ISSN	1558-4658 ; 0037-198X
ISSN (online)	1558-4658
ISSN	0037-198X
DOI	10.1053/j.ro.2023.01.005
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Article ; Online: Getting More Out of Large Databases and EHRs with Natural Language Processing and Artificial Intelligence: The Future Is Here.

Khosravi, Bardia / Rouzrokh, Pouria / Erickson, Bradley J

The Journal of bone and joint surgery. American volume

2022 Volume 104, Issue Suppl 3, Page(s) 51–55

Abstract: Electronic health records (EHRs) have created great opportunities to collect various information from clinical patient encounters. However, most EHR data are stored in unstructured form (e.g., clinical notes, surgical notes, and medication instructions), ...

Abstract	Electronic health records (EHRs) have created great opportunities to collect various information from clinical patient encounters. However, most EHR data are stored in unstructured form (e.g., clinical notes, surgical notes, and medication instructions), and researchers need data to be in computable form (structured) to extract meaningful relationships involving variables that can influence patient outcomes. Clinical natural language processing (NLP) is the field of extracting structured data from unstructured text documents in EHRs. Clinical text has several characteristics that mandate the use of special techniques to extract structured information from them compared with generic NLP methods. In this article, we define clinical NLP models, introduce different methods of information extraction from unstructured data using NLP, and describe the basic technical aspects of how deep learning-based NLP models work. We conclude by noting the challenges of working with clinical NLP models and summarizing the general steps needed to launch an NLP project.
MeSH term(s)	Humans ; Natural Language Processing ; Electronic Health Records ; Artificial Intelligence ; Databases, Factual
Language	English
Publishing date	2022-10-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	220625-0
ISSN	1535-1386 ; 0021-9355
ISSN (online)	1535-1386
ISSN	0021-9355
DOI	10.2106/JBJS.22.00567
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Article ; Online: THA-Net: A Deep Learning Solution for Next-Generation Templating and Patient-specific Surgical Execution.

Rouzrokh, Pouria / Khosravi, Bardia / Mickley, John P / Erickson, Bradley J / Taunton, Michael J / Wyles, Cody C

The Journal of arthroplasty

2023 Volume 39, Issue 3, Page(s) 727–733.e4

Abstract: Background: This study introduces THA-Net, a deep learning inpainting algorithm for simulating postoperative total hip arthroplasty (THA) radiographs from a single preoperative pelvis radiograph input, while being able to generate predictions either ... ...

Abstract	Background: This study introduces THA-Net, a deep learning inpainting algorithm for simulating postoperative total hip arthroplasty (THA) radiographs from a single preoperative pelvis radiograph input, while being able to generate predictions either unconditionally (algorithm chooses implants) or conditionally (surgeon chooses implants). Methods: The THA-Net is a deep learning algorithm which receives an input preoperative radiograph and subsequently replaces the target hip joint with THA implants to generate a synthetic yet realistic postoperative radiograph. We trained THA-Net on 356,305 pairs of radiographs from 14,357 patients from a single institution's total joint registry and evaluated the validity (quality of surgical execution) and realism (ability to differentiate real and synthetic radiographs) of its outputs against both human-based and software-based criteria. Results: The surgical validity of synthetic postoperative radiographs was significantly higher than their real counterparts (mean difference: 0.8 to 1.1 points on 10-point Likert scale, P < .001), but they were not able to be differentiated in terms of realism in blinded expert review. Synthetic images showed excellent validity and realism when analyzed with already validated deep learning models. Conclusion: We developed a THA next-generation templating tool that can generate synthetic radiographs graded higher on ultimate surgical execution than real radiographs from training data. Further refinement of this tool may potentiate patient-specific surgical planning and enable technologies such as robotics, navigation, and augmented reality (an online demo of THA-Net is available at: https://demo.osail.ai/tha_net).
MeSH term(s)	Humans ; Arthroplasty, Replacement, Hip/methods ; Hip Prosthesis ; Deep Learning ; Hip Joint/diagnostic imaging ; Hip Joint/surgery ; Radiography ; Retrospective Studies
Language	English
Publishing date	2023-08-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	632770-9
ISSN	1532-8406 ; 0883-5403
ISSN (online)	1532-8406
ISSN	0883-5403
DOI	10.1016/j.arth.2023.08.063
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Article ; Online: The impact of prior performance information on subsequent assessment: is there evidence of retaliation in an anonymous multisource assessment system?

Saberzadeh-Ardestani, Bahar / Sima, Ali Reza / Khosravi, Bardia / Young, Meredith / Mortaz Hejri, Sara

Advances in health sciences education : theory and practice

2023

Abstract: Few studies have engaged in data-driven investigations of the presence, or frequency, of what could be considered retaliatory assessor behaviour in Multi-source Feedback (MSF) systems. In this study, authors explored how assessors scored others if, ... ...

Abstract	Few studies have engaged in data-driven investigations of the presence, or frequency, of what could be considered retaliatory assessor behaviour in Multi-source Feedback (MSF) systems. In this study, authors explored how assessors scored others if, before assessing others, they received their own assessment score. The authors examined assessments from an established MSF system in which all clinical team members - medical students, interns, residents, fellows, and supervisors - anonymously assessed each other. The authors identified assessments in which an assessor (i.e., any team member providing a score to another) gave an aberrant score to another individual. An aberrant score was defined as one that was more than two standard deviations from the assessment receiver's average score. Assessors who gave aberrant scores were categorized according to whether their behaviour was preceded by: (1) receiving a score or not from another individual in the MSF system (2) whether the score they received was aberrant or not. The authors used a multivariable logistic regression model to investigate the association between the type of score received and the type of score given by that same individual. In total, 367 unique assessors provided 6091 scores on the performance of 484 unique individuals. Aberrant scores were identified in 250 forms (4.1%). The chances of giving an aberrant score were 2.3 times higher for those who had received a score, compared to those who had not (odds ratio 2.30, 95% CI:1.54-3.44, P < 0.001). Individuals who had received an aberrant score were also 2.17 times more likely to give an aberrant score to others compared to those who had received a non-aberrant score (2.17, 95% CI:1.39-3.39, P < 0.005) after adjusting for all other variables. This study documents an association between receiving scores within an anonymous multi-source feedback (MSF) system and providing aberrant scores to team members. These findings suggest care must be given to designing MSF systems to protect against potential downstream consequences of providing and receiving anonymous feedback.
Language	English
Publishing date	2023-07-24
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1352832-4
ISSN	1573-1677 ; 1382-4996
ISSN (online)	1573-1677
ISSN	1382-4996
DOI	10.1007/s10459-023-10267-2
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Article ; Online: A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI.

Faghani, Shahriar / Khosravi, Bardia / Moassefi, Mana / Conte, Gian Marco / Erickson, Bradley J

Journal of digital imaging

2023 Volume 36, Issue 3, Page(s) 837–846

Abstract: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The standard treatment for GBM consists of surgical resection followed by concurrent chemoradiotherapy and adjuvant temozolomide. O-6-methylguanine-DNA methyltransferase (MGMT) ...

Abstract	Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The standard treatment for GBM consists of surgical resection followed by concurrent chemoradiotherapy and adjuvant temozolomide. O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is an important prognostic biomarker that predicts the response to temozolomide and guides treatment decisions. At present, the only reliable way to determine MGMT promoter methylation status is through the analysis of tumor tissues. Considering the complications of the tissue-based methods, an imaging-based approach is preferred. This study aimed to compare three different deep learning-based approaches for predicting MGMT promoter methylation status. We obtained 576 T2WI with their corresponding tumor masks, and MGMT promoter methylation status from, The Brain Tumor Segmentation (BraTS) 2021 datasets. We developed three different models: voxel-wise, slice-wise, and whole-brain. For voxel-wise classification, methylated and unmethylated MGMT tumor masks were made into 1 and 2 with 0 background, respectively. We converted each T2WI into 32 × 32 × 32 patches. We trained a 3D-Vnet model for tumor segmentation. After inference, we constructed the whole brain volume based on the patch's coordinates. The final prediction of MGMT methylation status was made by majority voting between the predicted voxel values of the biggest connected component. For slice-wise classification, we trained an object detection model for tumor detection and MGMT methylation status prediction, then for final prediction, we used majority voting. For the whole-brain approach, we trained a 3D Densenet121 for prediction. Whole-brain, slice-wise, and voxel-wise, accuracy was 65.42% (SD 3.97%), 61.37% (SD 1.48%), and 56.84% (SD 4.38%), respectively.
MeSH term(s)	Adult ; Humans ; Glioblastoma/diagnostic imaging ; Glioblastoma/genetics ; Glioblastoma/pathology ; Temozolomide/therapeutic use ; Deep Learning ; Brain Neoplasms/diagnostic imaging ; Brain Neoplasms/genetics ; Brain Neoplasms/pathology ; DNA Methylation ; Brain/diagnostic imaging ; Magnetic Resonance Imaging/methods ; O(6)-Methylguanine-DNA Methyltransferase/genetics ; DNA Modification Methylases/genetics ; Tumor Suppressor Proteins/genetics ; DNA Repair Enzymes/genetics
Chemical Substances	Temozolomide (YF1K15M17Y) ; O(6)-Methylguanine-DNA Methyltransferase (EC 2.1.1.63) ; MGMT protein, human (EC 2.1.1.63) ; DNA Modification Methylases (EC 2.1.1.-) ; Tumor Suppressor Proteins ; DNA Repair Enzymes (EC 6.5.1.-)
Language	English
Publishing date	2023-01-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1033897-4
ISSN	1618-727X ; 0897-1889
ISSN (online)	1618-727X
ISSN	0897-1889
DOI	10.1007/s10278-022-00757-x
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Article ; Online: Outcome prediction based on initial CT scan in COVID-19.

Khosravi, Bardia / Sorouri, Majid / Abdollahi, Mohammad / Kasaeian, Amir / Radmard, Amir Reza

Heart & lung : the journal of critical care

2021 Volume 50, Issue 2, Page(s) 361–362

MeSH term(s)	COVID-19 ; Humans ; Lung ; Prognosis ; SARS-CoV-2 ; Tomography, X-Ray Computed
Language	English
Publishing date	2021-01-23
Publishing country	United States
Document type	Editorial ; Comment
ZDB-ID	193129-5
ISSN	1527-3288 ; 0147-9563
ISSN (online)	1527-3288
ISSN	0147-9563
DOI	10.1016/j.hrtlng.2021.01.013
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Article ; Online: Educational Overview of the Concept and Application of Computer Vision in Arthroplasty.

Vera-Garcia, Diana V / Nugen, Fred / Padash, Sirwa / Khosravi, Bardia / Mickley, John P / Erickson, Bradley J / Wyles, Cody C / Taunton, Michael J

The Journal of arthroplasty

2023 Volume 38, Issue 10, Page(s) 1954–1958

Abstract: Image data has grown exponentially as systems have increased their ability to collect and store it. Unfortunately, there are limits to human resources both in time and knowledge to fully interpret and manage that data. Computer Vision (CV) has grown in ... ...

Abstract	Image data has grown exponentially as systems have increased their ability to collect and store it. Unfortunately, there are limits to human resources both in time and knowledge to fully interpret and manage that data. Computer Vision (CV) has grown in popularity as a discipline for better understanding visual data. Computer Vision has become a powerful tool for imaging analytics in orthopedic surgery, allowing computers to evaluate large volumes of image data with greater nuance than previously possible. Nevertheless, even with the growing number of uses in medicine, literature on the fundamentals of CV and its implementation is mainly oriented toward computer scientists rather than clinicians, rendering CV unapproachable for most orthopedic surgeons as a tool for clinical practice and research. The purpose of this article is to summarize and review the fundamental concepts of CV application for the orthopedic surgeon and musculoskeletal researcher.
MeSH term(s)	Humans ; Arthroplasty ; Computers ; Orthopedic Procedures ; Orthopedics
Language	English
Publishing date	2023-08-25
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	632770-9
ISSN	1532-8406 ; 0883-5403
ISSN (online)	1532-8406
ISSN	0883-5403
DOI	10.1016/j.arth.2023.08.046
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Article ; Online: Quantifying Uncertainty in Deep Learning of Radiologic Images.

Faghani, Shahriar / Moassefi, Mana / Rouzrokh, Pouria / Khosravi, Bardia / Baffour, Francis I / Ringler, Michael D / Erickson, Bradley J

Radiology

2023 Volume 308, Issue 2, Page(s) e222217

Abstract: In recent years, deep learning (DL) has shown impressive performance in radiologic image analysis. However, for a DL model to be useful in a real-world setting, its confidence in a prediction must also be known. Each DL model's output has an estimated ... ...

Abstract	In recent years, deep learning (DL) has shown impressive performance in radiologic image analysis. However, for a DL model to be useful in a real-world setting, its confidence in a prediction must also be known. Each DL model's output has an estimated probability, and these estimated probabilities are not always reliable. Uncertainty represents the trustworthiness (validity) of estimated probabilities. The higher the uncertainty, the lower the validity. Uncertainty quantification (UQ) methods determine the uncertainty level of each prediction. Predictions made without UQ methods are generally not trustworthy. By implementing UQ in medical DL models, users can be alerted when a model does not have enough information to make a confident decision. Consequently, a medical expert could reevaluate the uncertain cases, which would eventually lead to gaining more trust when using a model. This review focuses on recent trends using UQ methods in DL radiologic image analysis within a conceptual framework. Also discussed in this review are potential applications, challenges, and future directions of UQ in DL radiologic image analysis.
MeSH term(s)	Humans ; Uncertainty ; Deep Learning ; Image Processing, Computer-Assisted ; Radiology
Language	English
Publishing date	2023-07-07
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	80324-8
ISSN	1527-1315 ; 0033-8419
ISSN (online)	1527-1315
ISSN	0033-8419
DOI	10.1148/radiol.222217
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Article ; Online: Demystifying Statistics and Machine Learning in Analysis of Structured Tabular Data.

Khosravi, Bardia / Weston, Alexander D / Nugen, Fred / Mickley, John P / Maradit Kremers, Hilal / Wyles, Cody C / Carter, Rickey E / Taunton, Michael J

The Journal of arthroplasty

2023 Volume 38, Issue 10, Page(s) 1943–1947

Abstract: Electronic health records have facilitated the extraction and analysis of a vast amount of data with many variables for clinical care and research. Conventional regression-based statistical methods may not capture all the complexities in high-dimensional ...

Abstract	Electronic health records have facilitated the extraction and analysis of a vast amount of data with many variables for clinical care and research. Conventional regression-based statistical methods may not capture all the complexities in high-dimensional data analysis. Therefore, researchers are increasingly using machine learning (ML)-based methods to better handle these more challenging datasets for the discovery of hidden patterns in patients' data and for classification and predictive purposes. This article describes commonly used ML methods in structured data analysis with examples in orthopedic surgery. We present practical considerations in starting an ML project and appraising published studies in this field.
MeSH term(s)	Humans ; Machine Learning ; Electronic Health Records
Language	English
Publishing date	2023-08-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	632770-9
ISSN	1532-8406 ; 0883-5403
ISSN (online)	1532-8406
ISSN	0883-5403
DOI	10.1016/j.arth.2023.08.045
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