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Evaluación del prompting de pocos disparos para la clasificación de sonidos pulmonares basada en espectrogramas

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Este resumen es generado por máquina.

Los modelos de lenguaje grandes (LLM) muestran potencial para la clasificación de sonidos pulmonares. El prompting de pocos disparos con GPT-4o mejoró la precisión en comparación con el de cero disparos, aunque el uso clínico requiere un mayor desarrollo.

Palabras clave:
inteligencia artificialprocesamiento de señales biomédicasmedicina respiratoriaclasificación de sonidos pulmonaresGPT-4oprompting de pocos disparosespectrogramas

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Área de la Ciencia:

  • Inteligencia Artificial
  • Procesamiento de Señales Biomédicas
  • Medicina Respiratoria

Sus antecedentes:

  • El aprendizaje profundo tradicional para el análisis de sonidos pulmonares requiere datos etiquetados extensos.
  • Los modelos de lenguaje grandes multimodales (LLM) ofrecen una alternativa potencial basada en prompts.
  • Es crucial evaluar los LLM de propósito general para la clasificación de sonidos respiratorios.

Objetivo del estudio:

  • Evaluar la utilidad de GPT-4o para la clasificación de sonidos pulmonares utilizando mel-espectrogramas.
  • Comparar el prompting de pocos disparos con el prompting de cero disparos para esta tarea.
  • Establecer una línea de base para la inferencia multimodal basada en prompts en acústica respiratoria.

Principales métodos:

  • Se convirtieron 6898 ciclos respiratorios anotados de la base de datos ICBHI 2017 en mel-espectrogramas.
  • Se empleó GPT-4o con estrategias de prompting de cero disparos y de pocos disparos para la clasificación.
  • Se evaluó el rendimiento utilizando precisión, exactitud, exhaustividad, puntuación F1 y la prueba de McNemar.

Principales resultados:

  • El prompting de pocos disparos arrojó mejoras estadísticamente significativas en la precisión (0,363 frente a 0,320) y otras métricas.
  • El análisis de repetibilidad del modelo mostró una alta concordancia (κ = 0,76-0,88), lo que indica una excelente consistencia.
  • Las ganancias de rendimiento fueron limitadas pero estadísticamente significativas (p < 0,001).

Conclusiones:

  • GPT-4o muestra potencial para la clasificación de sonidos pulmonares basada en prompts, superando el prompting de pocos disparos al de cero disparos.
  • El rendimiento actual es insuficiente para la implementación clínica, pero proporciona una base para la investigación futura.
  • La inferencia multimodal basada en prompts ofrece un enfoque flexible para el análisis de espectrogramas en medicina respiratoria.