Artificial Intelligence–Enabled Analysis of Thermography to Diagnose Acute Decompensated Heart Failure

JACC advances Pub Date : 2025-06-19 DOI:10.1016/j.jacadv.2025.101888

Andrés Nicolás Atamañuk MD, MSc , Ignacio Javier Gandino MD , María Noralí Miranda MD , Leandro Martín Cardozo MD , Sergio Exequiel Escalante MD , Cesar Villalba MD , David Abalovich Bernal , Gustavo Ross , Eduardo Perna MD , Diego Delgado MD

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引用次数: 0

Abstract

Background

Analyzing skin temperature in heart failure is an important medical practice that could assist to identify poor perfusion. Thermography, a technique that captures infrared radiation from tissues, could quantify these temperatures and thermal gradients. It has not been evaluated in patients with acute decompensated heart failure (ADHF) before.

Objectives

The purpose of this study was to assess the performance of thermography in the diagnosis of ADHF.

Methods

A cross-sectional study was performed, including consecutive patients hospitalized with ADHF diagnosed by an expert heart failure team. Patients hospitalized for other cardiac disorders without ADHF were included as controls. Ten thermal photos of each patient were taken within the first 4 hours after admission in a cardiac care unit. Specific thermal spots, averages, and gradients were analyzed. Thermography's diagnostic properties for ADHF detection were evaluated using machine learning with the extreme gradient boosting model.

Results

Sixty patients were included: 30 cases with ADHF and 30 controls. The mean age was 63.4 years (SD: 13.3), and 38 (63.3%) were males. Thermal points and averages showed lower temperature, while gradients were higher in the ADHF group, being all statistically significant between groups. The properties of the blend between thermography and artificial intelligence to detect ADHF had 84% sensitivity and 52% specificity. The area under the curve was 0.82 (95% CI: 0.73-0.91).

Conclusions

Thermography demonstrated differences between patients with ADHF and those with other cardiological disorders. In this proof of concept, combining thermography with artificial intelligence enabled the detection of ADHF in subjects hospitalized in a cardiac care unit.

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基于人工智能的热成像分析诊断急性失代偿性心力衰竭

分析心衰患者的皮肤温度是一项重要的医学实践，可以帮助识别灌注不良。热成像技术是一种从组织中捕获红外辐射的技术，可以量化这些温度和热梯度。以前还没有对急性失代偿性心力衰竭（ADHF）患者进行评估。目的探讨热成像技术在ADHF诊断中的应用价值。方法采用横断面研究，纳入经心衰专家小组诊断的连续住院ADHF患者。非ADHF的其他心脏疾病住院患者作为对照。每位患者在心脏护理病房入院后的头4小时内拍摄了10张热照片。分析了比热点、平均值和梯度。利用机器学习和极端梯度增强模型评估热成像对ADHF检测的诊断特性。结果共纳入60例患者：ADHF患者30例，对照组30例。平均年龄63.4岁（SD: 13.3），男性38例（63.3%）。热点和平均温度较低，ADHF组温度梯度较高，组间差异均有统计学意义。热成像与人工智能相结合检测ADHF的灵敏度为84%，特异性为52%。曲线下面积为0.82 （95% CI: 0.73-0.91）。结论热成像显示ADHF患者与其他心血管疾病患者存在差异。在这个概念验证中，将热成像与人工智能相结合，可以在心脏护理病房住院的受试者中检测ADHF。

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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来源期刊

JACC advances Cardiology and Cardiovascular Medicine

CiteScore

1.90

自引率

0.00%

发文量