与放射科住院医师相比,卷积神经网络与分析方法相结合的混合人工智能解决方案在胸外科患者肺部超声波 A 线检测中表现出更高的准确性。

Neuro endocrinology letters Pub Date : 2024-08-12
Martin Števík, Marek Malík, Štefánia Vetešková, Zuzana Trabalková, Maroš Hliboký, Michal Kolárik, Ján Magyar, Marek Bundzel, Martina Szabóová, František Babič, Marián Grendár, Kamil Zeleňák, Viktória Máčajová, Beáta Drobná Sániová, Anton Dzian
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引用次数: 0

摘要

目的:肺部超声可减少胸外科手术后胸部 X 射线的次数,从而减少辐射。COVID-19 大流行加速了利用人工智能检测肺部超声伪影的研究。本研究采用卷积神经网络和分析方法相结合的新型混合解决方案,评估了人工智能在胸外科患者 A 线检测中的准确性,并将其与放射科住院医师和放射科专家的结果进行了比较:前瞻性观察研究:单中心研究评估了人工智能和放射科住院医师在肺部超声波片段 A 线检测中的准确性,并与作为参考的两位放射科专家的一致意见进行了比较。住院医师初读后,将人工智能结果提交给住院医师,并要求他根据人工智能结果进行修改:连续 82 名患者接受了 82 次超声检查。结果:82 名患者连续接受了 82 次超声波检查,评估了 328 份超声波记录。人工智能在 A 线检测中的准确性、敏感性、特异性、阳性预测值和阴性预测值分别为 0.866、0.928、0.834、0.741 和 0.958。常驻值分别为 0.558、0.973、0.346、0.432 和 0.962。根据人工智能结果修正后的居民值分别为 0.854、0.991、0.783、0.701 和 0.994:人工智能在胸外科患者 A 线检测中表现出较高的准确性,与住院医师相比更为准确。人工智能可在胸外科患者肺部超声伪影检测和住院医师教育中发挥重要作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Artificial Intelligence Solution Combining Convolutional Neural Network and Analytical Approach Showed Higher Accuracy in A-lines Detection on Lung Ultrasound in Thoracic Surgery Patients Compared with Radiology Resident.

Objectives: Lung ultrasound reduces the number of chest X-rays after thoracic surgery and thus the radiation. COVID-19 pandemic has accelerated research in lung ultrasound artifacts detection using artificial intelligence. This study evaluates the accuracy of artificial intelligence in A-lines detection in thoracic surgery patients using a novel hybrid solution that combines convolutional neural networks and analytical approach and compares it with a radiology resident and radiology experts' results.

Design: Prospective observational study.

Material and methods: Single-center study evaluates the accuracy of artificial intelligence and a radiology resident in A-line detection on lung ultrasound footages compared with the consensual opinion of two expert radiologists as the reference. After resident's first reading, the artificial intelligence results were presented to the resident and he was asked to revise the results based on artificial intelligence.

Results: 82 consecutive patients underwent 82 ultrasound examinations. 328 ultrasound recordings were evaluated. Accuracy, sensitivity, specificity, positive and negative predictive values of artificial inelligence in A-line detection were 0.866, 0.928, 0.834, 0.741 and 0.958 respectively. The resident's values were 0.558, 0.973, 0.346, 0.432 and 0.962 respectively. The resident's values after correction based on artificial intelligence results were 0.854, 0.991, 0.783, 0.701 and 0.994 respectively.

Conclusion: Artificial intelligence showed high accuracy in A-line detection in thoracic surgery patients and was more accurate compared to a resident. Artificial intelligence could play important role in lung ultrasound artifact detection in thoracic surgery patients and in residents' education.

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