Fabijan Artur, Fabijan Robert, Zawadzka-Fabijan Agnieszka, Nowosławska Emilia, Zakrzewski Krzysztof, Polis Bartosz
Department of Neurosurgery, Polish-Mother's Memorial Hospital Research Institute, 93-338 Lodz, Poland.
Independent Researcher, Luton, LU2 0GS, UK.
Diagnostics (Basel). 2023 Jun 22;13(13):2142. doi: 10.3390/diagnostics13132142.
Assessing severe scoliosis requires the analysis of posturographic X-ray images. One way to analyse these images may involve the use of open-source artificial intelligence models (OSAIMs), such as the contrastive language-image pretraining (CLIP) system, which was designed to combine images with text. This study aims to determine whether the CLIP model can recognise visible severe scoliosis in posturographic X-ray images. This study used 23 posturographic images of patients diagnosed with severe scoliosis that were evaluated by two independent neurosurgery specialists. Subsequently, the X-ray images were input into the CLIP system, where they were subjected to a series of questions with varying levels of difficulty and comprehension. The predictions obtained using the CLIP models in the form of probabilities ranging from 0 to 1 were compared with the actual data. To evaluate the quality of image recognition, true positives, false negatives, and sensitivity were determined. The results of this study show that the CLIP system can perform a basic assessment of X-ray images showing visible severe scoliosis with a high level of sensitivity. It can be assumed that, in the future, OSAIMs dedicated to image analysis may become commonly used to assess X-ray images, including those of scoliosis.
评估严重脊柱侧弯需要分析姿势X线图像。分析这些图像的一种方法可能涉及使用开源人工智能模型（OSAIMs），例如对比语言-图像预训练（CLIP）系统，该系统旨在将图像与文本相结合。本研究旨在确定CLIP模型是否能够识别姿势X线图像中可见的严重脊柱侧弯。本研究使用了23张经两名独立神经外科专家评估的被诊断为严重脊柱侧弯患者的姿势图像。随后，将X线图像输入CLIP系统，在该系统中，它们要接受一系列难度和理解程度各异的问题。将使用CLIP模型以概率范围从0到1的形式获得的预测结果与实际数据进行比较。为了评估图像识别质量，确定了真阳性、假阴性和灵敏度。本研究结果表明，CLIP系统能够对显示可见严重脊柱侧弯的X线图像进行具有高灵敏度的基本评估。可以假定，未来专门用于图像分析的OSAIMs可能会普遍用于评估X线图像，包括脊柱侧弯的图像。
