目的:放射学报告的结论部分对于以自然语言总结主要放射学发现至关重要,对于向临床医生传达结果至关重要。然而,创建这些摘要非常耗时,重复,并且容易在不同的放射科医师之间出现差异和错误。为了解决这些问题,我们评估了一个经过微调的文本到文本转换转换器(T5)模型,用于抽象总结,以便以低资源语言自动生成神经放射学MRI报告的结论.
方法:我们将我们的方法应用于西班牙语的232,425例神经放射学MRI报告的数据集。我们比较了各种预训练的T5模型,包括多语种T5和新改编的西班牙语。为了精确评估,我们雇佣了BLEU,METEOR,ROUGE-L,CIDEr,和余弦相似性度量以及放射科专家评估。
结果:研究结果是有希望的,这些模型专门针对神经放射学MRI进行了微调,在BLEU-1,METEOR中得分分别为0.46、0.28、0.52、2.45和0.87,ROUGE-L,CIDEr,和余弦相似性度量,分别。在放射学专家的评估中,他们发现在75%的案例中,系统生成的结论与人工生成的结论一样好,甚至更好。
结论:这些方法证明了为神经放射学定制最先进的预训练模型的潜力和有效性,产生自动MRI报告结论,几乎符合专家质量。此外,这些结果强调了为放射学报告总结设计和预训练专用语言模型的重要性.
OBJECTIVE: The conclusion section of a radiology report is crucial for summarizing the primary radiological findings in natural language and essential for communicating results to clinicians. However, creating these summaries is time-consuming, repetitive, and prone to variability and errors among different radiologists. To address these issues, we evaluated a fine-tuned Text-To-Text Transfer Transformer (T5) model for abstractive summarization to automatically generate conclusions for neuroradiology MRI
reports in a low-resource language.
METHODS: We retrospectively applied our method to a dataset of 232,425 neuroradiology MRI
reports in Spanish. We compared various pre-trained T5 models, including multilingual T5 and those newly adapted for Spanish. For precise evaluation, we employed BLEU, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics alongside expert radiologist assessments.
RESULTS: The findings are promising, with the models specifically fine-tuned for neuroradiology MRI achieving scores of 0.46, 0.28, 0.52, 2.45, and 0.87 in the BLEU-1, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics, respectively. In the radiological experts\' evaluation, they found that in 75% of the cases evaluated, the conclusions generated by the system were as good as or even better than the manually generated conclusions.
CONCLUSIONS: The methods demonstrate the potential and effectiveness of customizing state-of-the-art pre-trained models for neuroradiology, yielding automatic MRI report conclusions that nearly match expert quality. Furthermore, these results underscore the importance of designing and pre-training a dedicated language model for radiology report summarization.