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Vol. 65. Issue 6.
Pages 509-518 (November - December 2023)
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Vol. 65. Issue 6.
Pages 509-518 (November - December 2023)
Original articles
Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia
Eficacia de la capacidad y la eficiencia pronósticas de la herramienta de inteligencia artificial Thoracic Care Suite de GE aplicada a la radiografía torácica de pacientes con neumonía COVID-19
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J.M. Plasencia-Martíneza,
Corresponding author
plasen79@gmail.com

Corresponding author.
, R. Pérez-Costab, M. Ballesta-Ruizc, J.M. García-Santosa
a Servicio de Radiología, Hospital General Universitario Morales Meseguer, Murcia, Spain
b Servicio de Medicina de Urgencias, Hospital General Universitario Morales Meseguer, Murcia, Spain
c Epidemiología y Salud Pública, Consejería de Salud Regional. IMIB-Arrixaca, Universidad de Murcia, Murcia, Spain
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Tables (3)
Table 1. Sample characteristics.
Table 2. Diagnostic performance of the number of lung fields in the first and second AI-CXR and daily radiological worsening rate in AI-CXR.
Table 3. Binary logistic regression models for the rate of radiological worsening of the CXR processed with the AI tool ≥0.5 lung fields (model 1) and number of affected lung fields in the second CXR processed with the AI tool (model 2).
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Abstract
Objective

Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient’s healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare’s Thoracic Care Suite (featuring Lunit INSIGHT CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays.

Methods

Outpatients with confirmed SARS-CoV-2 infection, with chest X-ray (CXR) findings probable or indeterminate for COVID-19 pneumonia, who required a second CXR due to unfavorableclinical course, were collected. The number of affected lung fields for the two CXRs was assessed using the AI tool.

Results

One hundred fourteen patients (57.4±14.2 years, 65−57%-men) were retrospectively collected. Fifteen (13.2%) required ventilatory support. Progression of pneumonic extension ≥0.5 lung fields per day compared to pneumonia onset, detected using the TCS tool, increased the risk of requiring ventilatory support by 4-fold. Analyzing the AI output required 26s of radiological time.

Conclusions

Applying the AI tool, Thoracic Care Suite, to CXR of patients with COVID-19 pneumonia allows us to anticipate ventilatory support requirements requiring less than half a minute.

Keywords:
Radiography
Prognoses
Coronavirus
COVID 19
AI (artificial intelligence)
Biomedical technology
Resumen
Objetivo

La rápida progresión de la neumonía por COVID-19 puede implicar la necesidad de recurrir a sistemas de respiración asistida, como, por ejemplo, la ventilación mecánica no invasiva o la intubación endotraqueal. La introducción de herramientas que detecten la neumonía por COVID-19 puede mejorar la atención sanitaria de los pacientes. Nuestro objetivo es evaluar la eficacia y la eficiencia de la herramienta de inteligencia artificial (IA) Thoracic Care Suite de GE Healthcare (que incorpora Lunit INSIGHT CXR) para predecir la necesidad de recurrir a la respiración asistida en función de la progresión de la neumonía en la COVID-19 en radiografías torácicas consecutivas.

Métodos

Se incluyeron pacientes ambulatorios con infección por SARS-CoV-2 confirmada, con hallazgos probables o indeterminados de neumonía por COVID-19 en la radiografía torácica (RXT) y que necesitaron someterse a una segunda RXT debido a la evolución clínica desfavorable. En las dos RXT se evaluaron el número de campos pulmonares afectados mediante la herramienta de IA.

Resultados

Se incluyeron 114 pacientes (57,4±14,2 años, 65−57 %- varones) de forma retrospectiva. 15 pacientes (el 13,2 %) precisaron respiración asistida. La progresión de la diseminación neumónica ≥0,5 campos pulmonares al día en comparación con el inicio de la neumonía, detectada mediante la herramienta TCS, cuadruplicó el riesgo de precisar respiración asistida. El análisis de los resultados de IA precisó 26 segundos.

Conclusiones

Aplicar la herramienta de IA, Thoracic Care Suite, a la RXT de pacientes con neumonía por COVID-19 nos permite predecir la necesidad de recurrir a la respiración asistida en menos de medio minuto.

Palabras clave:
Radiografía
Pronósticos
Coronavirus
COVID-19
IA (inteligencia artificial)
Tecnología biomédica

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