半自动化与全自动计算机断层扫描可扩展体成分分析技术在严重急性呼吸综合征冠状病毒-2患者中的评价

IF 2.6 Q3 NUTRITION & DIETETICS

Clinical nutrition ESPEN Pub Date : 2025-06-11 DOI:10.1016/j.clnesp.2025.06.006

Amy Wozniak , Paula O'Connor , Jared Seigal , Vasilios Vasilopoulos , Mirza Faisal Beg , Karteek Popuri , Cara Joyce , Patricia Sheean

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引用次数: 0

摘要

基本原理和目标：基于人工智能（AI）的全自动软件最近可用于可扩展的身体成分分析。在临床领域广泛应用之前，需要进行验证研究。我们的目标是在住院患者样本中比较全自动、基于人工智能的软件和半自动软件的结果。材料和方法：本回顾性队列纳入了不同组的冠状病毒-2 （COVID-19）患者和可评估的计算机断层扫描（CT）图像。我们的目标是比较身体成分的多个方面，从全自动和半自动身体成分软件获取结果。使用Bland-Altman分析和相关系数计算骨骼肌（SM）、内脏脂肪组织（VAT）、皮下脂肪组织（SAT）、肌间脂肪组织（IMAT）和总脂肪组织（tat - SAT、VAT和IMAT的总和）的平均偏倚和偏倚趋势。结果：共有141例患者（平均（标准差（SD））年龄为58.2岁（18.9岁），61%为男性，31%为白人非西班牙裔，31%为黑人非西班牙裔，33%为西班牙裔）参与了分析。平均偏差（mean±SD）较小（与SD相比），SM为负（-3.79 cm2±7.56 cm2）， SAT为负（-7.06 cm2±19.77 cm2）， VAT为正（2.29 cm2±15.54 cm2）。在IMAT中观察到较大的负偏倚（-7.77 cm2±5.09 cm2），其中全自动软件相对于半自动软件低估了肌肉内组织数量。在相关系数为-0.625的情况下，IMAT计算的差异在其范围内不均匀；随着平均IMAT的增加，偏差（全自动软件的低估）更大。结论：与半自动化软件相比，基于人工智能的全自动软件提供了关键CT身体成分测量（SM， SAT， VAT， TAT）的一致结果。虽然我们的研究结果通过小偏差和有限的异常值证明了良好的总体一致性，但需要在其他临床人群中进行额外的研究，以进一步支持有效性和更高的准确性，特别是在身体成分和营养不良评估的背景下。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Evaluation of semi-automated versus fully automated technologies for computed tomography scalable body composition analyses in patients with severe acute respiratory syndrome Coronavirus-2

Rationale and objectives

Fully automated, artificial intelligence (AI) -based software has recently become available for scalable body composition analysis. Prior to broad application in the clinical arena, validation studies are needed. Our goal was to compare the results of a fully automated, AI-based software with a semi-automatic software in a sample of hospitalized patients.

Materials and methods

A diverse group of patients with Coronovirus-2 (COVID-19) and evaluable computed tomography (CT) images were included in this retrospective cohort. Our goal was to compare multiple aspects of body composition procuring results from fully automated and semi-automated body composition software. Bland-Altman analyses and correlation coefficients were used to calculate average bias and trend of bias for skeletal muscle (SM), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tissue (IMAT), and total adipose tissue (TAT-the sum of SAT, VAT, and IMAT).

Results

A total of 141 patients (average (standard deviation (SD)) age of 58.2 (18.9), 61 % male, and 31 % White Non-Hispanic, 31 % Black Non-Hispanic, and 33 % Hispanic) contributed to the analysis. Average bias (mean ± SD) was small (in comparison to the SD) and negative for SM (−3.79 cm² ± 7.56 cm²) and SAT (−7.06 cm² ± 19.77 cm²), and small and positive for VAT (2.29 cm² ± 15.54 cm²). A large negative bias was observed for IMAT (−7.77 cm² ± 5.09 cm²), where fully automated software underestimated intramuscular tissue quantity relative to the semi-automated software. The discrepancy in IMAT calculation was not uniform across its range given a correlation coefficient of −0.625; as average IMAT increased, the bias (underestimation by fully automated software) was greater.

Conclusions

When compared to a semi-automated software, a fully automated, AI-based software provides consistent findings for key CT body composition measures (SM, SAT, VAT, TAT). While our findings support good overall agreement as evidenced by small biases and limited outliers, additional studies are needed in other clinical populations to further support validity and advanced precision, especially in the context of body composition and malnutrition assessment.

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来源期刊

Clinical nutrition ESPEN NUTRITION & DIETETICS-

CiteScore

4.90

自引率

3.30%

发文量

512

期刊介绍： Clinical Nutrition ESPEN is an electronic-only journal and is an official publication of the European Society for Clinical Nutrition and Metabolism (ESPEN). Nutrition and nutritional care have gained wide clinical and scientific interest during the past decades. The increasing knowledge of metabolic disturbances and nutritional assessment in chronic and acute diseases has stimulated rapid advances in design, development and clinical application of nutritional support. The aims of ESPEN are to encourage the rapid diffusion of knowledge and its application in the field of clinical nutrition and metabolism. Published bimonthly, Clinical Nutrition ESPEN focuses on publishing articles on the relationship between nutrition and disease in the setting of basic science and clinical practice. Clinical Nutrition ESPEN is available to all members of ESPEN and to all subscribers of Clinical Nutrition.