Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia.

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.

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 / Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia

Objetivo: La rápida progresión de la neumonía COVID-19 puede implicar la necesidad de recurrir a sistemas de respiración asistida, como la ventilación mecánica no invasiva o la intubación endotraqueal. La introducción de herramientas que detecten la neumonía 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 incluyó a pacientes ambulatorios con infección por SARS-CoV-2 confirmada, con hallazgos probables o indeterminados de neumonía COVID-19 en la radiografía torácica (RXT) y que necesitaron una segunda RXT debido a la evolución clínica desfavorable. En las 2RXT se evaluaron el número de campos pulmonares afectados mediante la herramienta de IA. Resultados: Se incluyó a 114 pacientes (57,4±14,2 años; 65 de ellos varones, el 57%) 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 COVID-19 nos permite predecir la necesidad de recurrir a la respiración asistida en menos de medio minuto.(AU)

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 unfavorable clinical course, were collected. The number of affected lung fields for the 2CXRs was assessed using the AI tool. Results: One hundred fourteen patients (57.4±14.2 years; 65 of them were men, 57%) were retrospectively collected; and 15 (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 26seconds 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.(AU)

Cronología / Chronology

Esta cronología es una idea del psicoanalista e investigador francés Théo Lucciardi y fue publicada originalmente en el número 3 de la revista LAPSUS NUMÉRIQUE. Su autor ha preparado esta versión actualizada a 2023 especialmente para este número de Aesthethica. La secuencia, que va desde la invención de la rueda hasta la IA generativa, permite detenernos en los grandes hitos del desarrollo científico tecnológico y a la vez advertir ve el grado de aceleración de la última década. Se pueden reconocer allí varios de los temas que integran la agenda contemporánea en materia de bioética y que están presentes en este número de la revista. Algunos de ellos son cruciales para la lectura ético-analítica que proponemos, como la vigencia de la lógica booleana, la actualización del Test de Turing o el porvenir de la IA y el Chat GPT

This chronology is an initiative of the French psychoanalyst and researcher Théo Lucciardi and was originally published in number 3 of the LAPSUS NUMÉRIQUE magazine. Its author has prepared this updated version to 2023 especially for this issue of Aesthethica. The sequence, which goes from the invention of the wheel to generative AI, allows us to stop at the great milestones of technological scientific development and at the same time notice the degree of acceleration of the last decade. Several of the issues that make up the contemporary agenda in bioethics and that are present in this issue of the magazine can be recognized there. Some of them are crucial for the ethical-analytical reading that we propose, such as the validity of Boolean logic, the updating of the Turing Test or the future of AI and Chat GPT