The value of radiomics to predict abnormal bone mass in type 2 diabetes mellitus patients based on CT imaging for paravertebral muscles

Qiu, Haijun; Yang, Hui; Yang, Zhe; Yao, Qianqian; Duan, Shaofeng; Qin, Jian; Zhu, Jian

doi:10.3389/fendo.2022.963246

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Various imaging studies in different fields have been published, highlighting the potential of radiomics to enhance clinical decision-making. This technique has been used in a variety of fields in the health care system such as in brain tumor/pathology [ 26 27 ], colorectal carcinoma [ 28 29 30 31 ], coronary heart disease [ 32 33 ], and diabetes [ 34 ].…”

Section: Artificial Intelligence: Machine Learning Neural Network And...mentioning

confidence: 99%

Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics

Ghayda

Cannarella

Calogero

et al. 2024

World J Mens Health

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Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.

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Section: Artificial Intelligence: Machine Learning Neural Network And...mentioning

confidence: 99%

Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics

Ghayda

Cannarella

Calogero

et al. 2024

World J Mens Health

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“…Radiomics is a widely applied technique in clinical oncology for tasks such as cancer detection and diagnosis, forecasting outcomes and prognosis, and predicting post-treatment response [154][155][156]. Recently, several studies have emerged in which textural characteristics extracted from X-rays, MRI scans, and DEXA scans have been applied to detect, diagnose, and classify osteoporosis and other metabolic bone disorders [141][142][143][144]. However, the diagnostic accuracy for osteoporosis on these modalities remains inadequate, with reported values for the area under the curve (AUC) in osteoporosis classification typically hovering around 0.8.…”

Section: Advantages and Efficacymentioning

confidence: 99%

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Ong,

Liu,

Makmur

et al. 2023

Bioengineering

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Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.

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“…The type 2 diabetes mellitus (T2DM) condition is a multifaceted and enduring metabolic disorder distinguished by elevated blood sugar levels. It ranks among the most prevalent chronic ailments encountered in clinical settings, with the potential to give rise to complications affecting multiple organs [ 1 , 2 ]. The process of nonenzymatic protein glycation is a significant contributor to the emergence of complications associated with DM [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Evaluation of Achilles Tendon Thickness in Diabetic Patients With and Without Foot Complications

Prajwal,

Sakalecha,

Dudekula

et al. 2024

Cureus

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Introduction Diabetes mellitus (DM) is a multifaceted metabolic disorder distinguished by elevated blood sugar levels. Type 2-DM (T2DM) stands as a significant contributor to disability due to its widespread occurrence of microvascular and macrovascular complications. According to certain researchers, prolonged elevated blood sugar levels have been observed to trigger a sequence of irregular alterations in the Achilles tendon (AT). AT thickness is one such indicator of these alterations. Methods This was a prospective study carried out on 51 individuals which was further divided into 3 groups (Group A - Normal individuals, Group B - DM without foot complications, Group C - DM with foot complications) with 17 individuals in each as healthy, DM with foot complications and without complications at Sri Devaraj Urs Medical College over a period of one year. The patients' demographics, basic medical records, and laboratory test results were examined and analyzed. Results The mean age of the study participants was 55.41 + 10.25 years. There was no significant difference in age and gender between the three groups. There was a significant difference in mean AT thickness between the groups (p<0.05). The mean thickness of AT was higher in Group C compared to Group B. Group A had the least AT thickness. However, there was no correlation between the variables in individual groups. Conclusions Modifications in the AT's structure may occur before the onset of foot and ankle issues in individuals with diabetes. Hence, the thickening of the AT can be used as an early indicator of impending diabetic foot complications.

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The value of radiomics to predict abnormal bone mass in type 2 diabetes mellitus patients based on CT imaging for paravertebral muscles

Cited by 7 publications

References 19 publications

Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics

Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Ultrasound Evaluation of Achilles Tendon Thickness in Diabetic Patients With and Without Foot Complications

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