Deep Radiomics Features of Median Nerves for Automated Diagnosis of Carpal Tunnel Syndrome With Ultrasound Images: A Multi‐Center Study

Mohammadi, Afshin; Torres-Cuenca, Thomas; Mirza‐Aghazadeh‐Attari, Mohammad; Faeghi, Fariborz; Acharya, U. Rajendra; Ardakani, Ali Abbasian

doi:10.1002/jum.16244

Cited by 6 publications

(7 citation statements)

References 32 publications

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“…Other study explores the application of deep radiomics in diagnosing CTS by focusing on analyzing the deep radiomics features of median nerves using ultrasound images, which could potentially automate and improve the accuracy of CTS diagnosis ( 25 ).…”

Section: Ai-enhanced Pns Disorders Diagnosticsmentioning

confidence: 99%

Transforming medicine: artificial intelligence integration in the peripheral nervous system

Qian,

Alhaskawi,

Dong

et al. 2024

Front. Neurol.

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In recent years, artificial intelligence (AI) has undergone remarkable advancements, exerting a significant influence across a multitude of fields. One area that has particularly garnered attention and witnessed substantial progress is its integration into the realm of the nervous system. This article provides a comprehensive examination of AI’s applications within the peripheral nervous system, with a specific focus on AI-enhanced diagnostics for peripheral nervous system disorders, AI-driven pain management, advancements in neuroprosthetics, and the development of neural network models. By illuminating these facets, we unveil the burgeoning opportunities for revolutionary medical interventions and the enhancement of human capabilities, thus paving the way for a future in which AI becomes an integral component of our nervous system’s interface.

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Section: Ai-enhanced Pns Disorders Diagnosticsmentioning

confidence: 99%

Transforming medicine: artificial intelligence integration in the peripheral nervous system

Qian,

Alhaskawi,

Dong

et al. 2024

Front. Neurol.

View full text Add to dashboard Cite

show abstract

“…The machine learning for selection of hyperparameters encompassed various algorithms, such as support vector machines, regression models, random forests, K-nearest neighbors (KNN), and Bayesian methods. One of the included studies [23] utilized a deep learning model consisting of SqueezeNet.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Concerning the seven criteria in the QUADAS-2 tool, two studies [21,24] did not meet the criteria for assessing the risk of bias related to flow and timing. Only three studies [20,22,23] clearly defined a specific time interval between the electrodiagnostic test and the ultrasound examination. In three studies conducted by Lyu et al [21,23,24], there was a lack of specification regarding exclusion criteria, which raised concerns about the applicability of their patient selection process.…”

Section: Quality Assessmentmentioning

confidence: 99%

“…Only three studies [20,22,23] clearly defined a specific time interval between the electrodiagnostic test and the ultrasound examination. In three studies conducted by Lyu et al [21,23,24], there was a lack of specification regarding exclusion criteria, which raised concerns about the applicability of their patient selection process. Furthermore, three [19,20,23] of these studies provided specific protocols for radiologists to evaluate the quality of median nerves, e.g., assessing nerve honeycomb pattern and echogenicity.…”

Section: Quality Assessmentmentioning

confidence: 99%

“…In three studies conducted by Lyu et al [21,23,24], there was a lack of specification regarding exclusion criteria, which raised concerns about the applicability of their patient selection process. Furthermore, three [19,20,23] of these studies provided specific protocols for radiologists to evaluate the quality of median nerves, e.g., assessing nerve honeycomb pattern and echogenicity. In the context of these studies, radiomics features were extracted using MATLAB software (version R2019a, MathWorks Inc., Natick, MA, USA), Python, and Novo Ultrasound Kit (GE Institute of Precision Medicine, IPM) software, v. 3.12.0.…”

Section: Quality Assessmentmentioning

confidence: 99%

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The Potential of Ultrasound Radiomics in Carpal Tunnel Syndrome Diagnosis: A Systematic Review and Meta-Analysis

Wu,

Lin,

Shu

et al. 2023

Diagnostics

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Background: Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy for which ultrasound imaging has recently emerged as a valuable diagnostic tool. This meta-analysis aims to investigate the role of ultrasound radiomics in the diagnosis of CTS and compare it with other diagnostic approaches. Methods: We conducted a comprehensive search of electronic databases from inception to September 2023. The included studies were assessed for quality using the Quality Assessment Tool for Diagnostic Accuracy Studies. The primary outcome was the diagnostic performance of ultrasound radiomics compared to radiologist evaluation for diagnosing CTS. Results: Our meta-analysis included five observational studies comprising 840 participants. In the context of radiologist evaluation, the combined statistics for sensitivity, specificity, and diagnostic odds ratio were 0.78 (95% confidence interval (CI), 0.71 to 0.83), 0.72 (95% CI, 0.59 to 0.81), and 9 (95% CI, 5 to 15), respectively. In contrast, the ultrasound radiomics training mode yielded a combined sensitivity of 0.88 (95% CI, 0.85 to 0.91), a specificity of 0.88 (95% CI, 0.84 to 0.92), and a diagnostic odds ratio of 58 (95% CI, 38 to 87). Similarly, the ultrasound radiomics testing mode demonstrated an aggregated sensitivity of 0.85 (95% CI, 0.78 to 0.89), a specificity of 0.80 (95% CI, 0.73 to 0.85), and a diagnostic odds ratio of 22 (95% CI, 12 to 41). Conclusions: In contrast to assessments by radiologists, ultrasound radiomics exhibited superior diagnostic performance in detecting CTS. Furthermore, there was minimal variability in the diagnostic accuracy between the training and testing sets of ultrasound radiomics, highlighting its potential as a robust diagnostic tool in CTS.

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Interpretation of Artificial Intelligence Models in Healthcare

Abbasian Ardakani,

Airom,

Khorshidi

et al. 2024

J of Ultrasound Medicine

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Artificial intelligence (AI) models can play a more effective role in managing patients with the explosion of digital health records available in the healthcare industry. Machine‐learning (ML) and deep‐learning (DL) techniques are two methods used to develop predictive models that serve to improve the clinical processes in the healthcare industry. These models are also implemented in medical imaging machines to empower them with an intelligent decision system to aid physicians in their decisions and increase the efficiency of their routine clinical practices. The physicians who are going to work with these machines need to have an insight into what happens in the background of the implemented models and how they work. More importantly, they need to be able to interpret their predictions, assess their performance, and compare them to find the one with the best performance and fewer errors. This review aims to provide an accessible overview of key evaluation metrics for physicians without AI expertise. In this review, we developed four real‐world diagnostic AI models (two ML and two DL models) for breast cancer diagnosis using ultrasound images. Then, 23 of the most commonly used evaluation metrics were reviewed uncomplicatedly for physicians. Finally, all metrics were calculated and used practically to interpret and evaluate the outputs of the models. Accessible explanations and practical applications empower physicians to effectively interpret, evaluate, and optimize AI models to ensure safety and efficacy when integrated into clinical practice.

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Deep Radiomics Features of Median Nerves for Automated Diagnosis of Carpal Tunnel Syndrome With Ultrasound Images: A Multi‐Center Study

Cited by 6 publications

References 32 publications

Transforming medicine: artificial intelligence integration in the peripheral nervous system

Transforming medicine: artificial intelligence integration in the peripheral nervous system

The Potential of Ultrasound Radiomics in Carpal Tunnel Syndrome Diagnosis: A Systematic Review and Meta-Analysis

Interpretation of Artificial Intelligence Models in Healthcare

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