The joint study of Chang Gung Memorial Hospital and aetherAI was published by Scientific Reports

Updated: Aug 26, 2021

April 8, 2021

This article, Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs, co-authored by aetherAI and Chang Gung Memorial Hospital was accepted and published by a prestigious journal, Scientific Report as recognized its deep learning approach breakthrough.

Human spinal balance assessment relies considerably on sagittal radiographic parameter measurement. Deep learning could be applied for automatic landmark detection and alignment analysis, with mild to moderate standard errors and favourable correlations with manual measurement. In this study, based on 2210 annotated images of various spinal disease aetiologies, it developed deep learning models capable of automatically locating 45 anatomic landmarks and subsequently generating 18 radiographic parameters on a whole-spine lateral radiograph.

(A schematic diagram of the pipeline for parameter estimation is illustrated.)

In the assessment of model performance, the localization accuracy and learning speed were the highest for landmarks in the cervical area, followed by those in the lumbosacral, thoracic, and femoral areas. The proposed automatic alignment analysis system was able to localise spinal anatomic landmarks with high accuracy and to generate various radiographic parameters with favourable correlations with manual measurements.

Full article

雲象和長庚骨科的合作研究,用AI進行體側X光脊椎特徵點偵測的研究論文在Scientific Reports正式刊出!

骨科醫師在脊椎矯正手術前,必須對脊椎進行複雜的量測,以利制定手術計畫。傳統的量測方法是用手工的方式標註數十個特徵點,接著依據這些特徵點計算出各種脊椎重要的參數。傳統手工標註量測的方式非常不便且費時。雲象科技和長庚骨科合作,藉由醫師精確標註的,世界最大、兩千兩百張體側X光影像資料集,訓練深度神經網路(AI)進行45個脊椎特徵點的辨識。AI 在經過訓練之後,定位脊椎特徵點可以達到非常高的精確度,而且整個分析程序在數秒鐘之內即可完成。