Supporting data for "Incorporating Artificial Intelligence and Clinical Informatics for Curve Progression Risk Evaluation in Adolescent Idiopathic Scoliosis to Facilitate Population Screening"

Adolescent idiopathic scoliosis (AIS) is a complex three-dimensional spinal deformity affecting 2-5% of the general population. AIS is diagnosed when Cobb angles exceed 10°, and deterioration of curve magnitude during puberty occurs in two-thirds of patients. Whilst curves 45° are severe and indicated for surgical correction. A student screening system for AIS has been adopted in Hong Kong since the 1990's. Patients are commonly diagnosed via such screening in their early teens when the curvature is mild, yet it remains uncertain which curves will continue to deteriorate upon the remaining period of growth. Prediction of curve progression risk in AIS remains elusive. Prior studies have revealed the potential for three-dimensional (3D) morphological parameters to prognosticate progression, but these require specialized biplanar imaging equipment and labor-intensive software reconstruction. In addition, patient demographics, vertebral morphology, skeletal maturity, and bone quality represent individual risk factors for progression but have yet to be integrated towards accurate prognostication. The objective of this study was to integrate composite clinical information into deep learning model to accurately predict AIS curves at-risk of progression.The dataset contains 710 AIS patients receiving regular clinical follow-up in 3-6-month intervals at the Duchess of Kent Children's Hospital (DKCH), enabling labelling of major curve trajectories from first clinic presentation until skeletal maturity. Additional inclusion criteria were (1) diagnosis of AIS, (2) Cobb angle between 11° and 30° upon standing posteroanterior X-rays at first visit, (3) DRU grading ≤ R9U8 to demonstrate growth potential, and (4) regular follow-up concluding at skeletal maturity (R11U9) or upon receiving surgery. This cohort was utilised to develop the spinal X-ray radiomics modules and identified from amongst scoliosis clinic attendees between January 2016 and September 2021, of which more than 90% were referrals from the two-tiered Hong Kong school-aged screening program. Only the major curve with largest Cobb angle was considered for patients with more than one curvature. Curve progression was defined by an increase ≥ 6° between first visit and skeletal maturity, as well as a Cobb angle ≥ 25° at skeletal maturity. Non-progression (NP) was defined by < 6° of curvature increase, or a Cobb angle < 25° at skeletal maturity. Patients with non-progressive curves according to these definitions but received brace treatment were also excluded. In preparation for automated hand X-ray analysis, an experienced orthopedic researcher used online labeling tool Roboflow to label regions of interest (ROIs) corresponding to (1) the 2nd to 4th metacarpals, (2) distal radial physis, and (3) distal ulnar physis. Pixel-level segmentation labels of the second metacarpus and the corresponding intramedullary mid diaphysis were subsequently labelled. Skeletal maturity indices (DRU and Sanders staging) from both the hand X-ray cohort as well as curve progression cohort were labelled by two experienced orthopedic researchers.Features contained within the major curve apex of PA spinal radiographs predict curve progression due to their capacity to convey rotation and torsion. On the other hand, whole spine X-rays facilitate assessment of global spinal imbalance as a risk factor for curve progression. Thus, we extracted a regional spinal X-ray ROI (300 × 200-pixel fixed window) centered upon the apical vertebrae/disc of the major curve, together with at least two adjacent vertebras above and below with lateral rib articulations. We also extracted a global spinal X-ray ROI (300 × 300-pixel fixed window) covering T1 to the sacrum together with clavicles, ribs, and pelvis. All ROI images were saved as single channel grayscale image in JPG formatting.

青少年特发性脊柱侧弯（AIS）是一种复杂的立体三维脊柱畸形，影响普通人群的2%-5%。AIS的诊断标准为Cobb角超过10°，在青春期，三分之二的患者的弯曲程度会恶化。尽管45°的弯曲属于严重病例，需要手术矫正。自20世纪90年代以来，香港已经采纳了AIS的学生筛查系统。患者通常在青春期早期通过这种筛查被诊断，此时弯曲程度较轻，然而，尚不清楚哪些弯曲会在剩余的生长期间继续恶化。预测AIS弯曲进展风险仍然充满挑战。先前的研究表明，三维（3D）形态学参数有望预测进展，但这些参数需要专业的双平面成像设备和劳动密集型的软件重建。此外，患者的人口统计学特征、椎骨形态、骨骼成熟度和骨骼质量是进展的个体风险因素，但尚未整合以实现准确的预测。本研究的目的是将综合临床信息整合到深度学习模型中，以准确预测AIS中存在进展风险的弯曲。该数据集包含710名在肯特公爵夫人儿童医院（DKCH）接受定期临床随访的AIS患者，随访间隔为3-6个月，从首次就诊到骨骼成熟期，能够对主要的弯曲轨迹进行标注。额外的纳入标准包括：（1）AIS的诊断，（2）首次就诊时站立位的后前位X光片上的Cobb角在11°至30°之间，（3）DRU分级≤R9U8以显示生长潜力，（4）定期随访直至骨骼成熟（R11U9）或接受手术。这一队列被用于开发脊柱X光影像组学模块，并从2016年1月至2021年9月间参加脊柱侧弯诊所的患者中筛选出来，其中超过90%的患者来自香港两级的学龄儿童筛查计划。对于有多处弯曲的患者，仅考虑Cobb角最大的主要弯曲。弯曲进展定义为首次就诊与骨骼成熟期之间增加≥6°，以及骨骼成熟期的Cobb角≥25°。非进展（NP）定义为弯曲增加<6°，或在骨骼成熟期的Cobb角<25°。根据这些定义，虽然曲线没有进展，但接受了支具治疗的患者也被排除在外。为了准备自动手动X光分析，一位经验丰富的骨科研究员使用在线标注工具Roboflow对感兴趣区域（ROI）进行标注，这些区域对应于（1）第二至第四掌骨，（2）桡骨远端骨骺，（3）尺骨远端骨骺。随后，对第二掌骨和相应的髓内中骨干部的像素级分割标签进行了标注。来自手部X光队列和弯曲进展队列的骨骼成熟度指数（DRU和Sanders分期）由两位经验丰富的骨科研究员进行标注。PA脊柱X光影像中主要弯曲顶部的特征可以预测弯曲进展，因为它们能够传达旋转和扭曲。另一方面，全身脊柱X光片有助于评估整体脊柱失衡，这是弯曲进展的风险因素。因此，我们提取了以主要弯曲顶部的椎体/间盘为中心的区域脊柱X光ROI（300×200像素固定窗口），以及至少两个相邻的椎骨和肋骨的侧关节。我们还提取了一个全球脊柱X光ROI（300×300像素固定窗口），覆盖从T1到骶骨，以及锁骨、肋骨和骨盆。所有ROI图像均保存为单通道灰度图像，格式为JPG。