Uso da inteligência artificial como armamento no diagnóstico e condução clínica da estenose de canal lombar

Resumo

A estenose do canal vertebral lombar (ECL) é uma patologia complexa, com alta incidência entre pessoas acima de 65 anos de idade. No entanto, o diagnóstico correto é, por vezes, difícil de ser confirmado. O uso de modelos de Inteligência Artificial (IA) na medicina é, em geral, desconhecida para a maioria da comunidade médica, mas tem sido usada há décadas na assistência em UTI, os métodos de imagem e dispositivos de diagnóstico eletrônico (ECG). Através de uma revisão sistemática da literatura, com foco nos achados clínicos e radiológicos, juntamente com todas as modalidades de tratamento, foi possível identificar o ambiente completo de pacientes LSS, para responder a quatro questões: (a) “Com base no quadro clínico, o paciente tem um, cenário moderado ou grave?”; (b) “Com base nos dados radiológicos, o paciente pode ser classificado com um cenário leve, moderada ou grave?”; (c) “Qual é a probabilidade, com base na anamnese, do paciente ter ECL?”; (d) “Qual é o melhor tratamento a ser oferecido?”.+Com o auxílio de um software usando Sistema Especialista (Expert Sinta), uma linguagem de IA, alocamos todas as variáveis e seus valores para orientar o software responder às quatro perguntas. Foi possível identificar 657 artigos científicos, no entanto apenas 63 poderia mencionar não apenas as variáveis, mas a sua probabilidade de ocorrência ou teve disponibilidade texto completo. Foi possível classificar a intensidade do quadro clínico e radiológico, criar um índice de probabilidade para LSS e oferecer o melhor tratamento. Recomendamos o uso, sob supervisão médica, em de Neurocirurgia ou clínicas ortopédicas como um conselheiro para os pacientes com ELA.

Abstract

The lumbar spinal stenosis (LSS) is a complex pathology with high incidence among people above 65 years old. However, the correct diagnose is sometimes difficult to perform. The use of Artificial Intelligence (AI) models in medicine is, in general, unfamiliar for the majority of medical community, but has been used for decades in assistance in ICUs, image methods and electronic diagnostic devices (EKG). Through a systematic literature review focused in the clinical and radiological findings, in addition to all treatment modalities, we identified the complete environment of LSS patients, to answer four questions. (a) “Based on the clinical presentation, the patient has a mild, moderate or severe scenario?”, (b) “Based on the radiological data, the patient can be classified having a mild, moderate or severe scenario?”, (c) “What is the probability, based on the anamneses, the patient has LSS?”, and (d) “What is the best treatment to be offered?”. With the aid of a software using Expert System (Expert Sinta), a language of AI, we allocate all the variables and their values to orient the software to answer the four questions. It was possible to identify 657 scientific articles, however only 63 could mention not only the variables, but their occurrence probability or had full text availability. It was possible to classify the intensity the clinical and radiological scenario, create a probability index for LSS and offer the best treatment. We recommend the use, under medical supervision, in neurosurgery or orthopedic clinics as an adviser for patients with LSS.

Palavras-Chave

estenoses de canal lombar - inteligência artificial - sistema especialista - diagnóstico - tratamento

Keywords

lumbar spinal stenosis - artificial intelligence - expert system - diagnosis - treatment

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