Machine Learning Driven Diagnosis of Low Bone Density on Plain-Film X-Rays

骨质疏松症的特征是骨量的退化和骨微结构的改变。据估计，全球有超过2亿人患有骨质疏松症，由于人口结构的变化，这一数字预计还会稳步增加。如果不进行治疗，骨质疏松症可能会导致骨折，这不仅会使患者致残，而且每年还会给护理系统造成数百万美元的损失。令人震惊的是，50岁以上的女性中有1/3和男性中有1/5会在一生中经历骨质疏松性骨折，因此，医疗保健系统必须做好准备，尽早识别骨量丢失，以减少骨折的发生率。骨折如此常见的一个原因是，约75%(2)的骨质疏松患者仍然没有得到诊断(由于缺乏筛查机会)，因此错过了接受治疗以降低骨折风险的机会。骨质疏松症和脆性骨折造成的公共卫生负担日益增加，突显了立即在医疗保健系统中优先处理骨质疏松症的必要性。由于骨质疏松症是高度可预防的，注重有效的管理为未来节省大量资金提供了机会。为了减轻骨质疏松症的医疗负担，并将骨折的风险降至最低，需要及早发现患者，以便他们能够接受有效的治疗和干预。我们提出的解决方案OsteoSight是一个机器学习(AI)模型，它将拍摄一张X光图像，分析图像中的骨骼结构，并将骨骼标记为“骨质疏松”或“没有骨质疏松”。这种方法意味着，在二级护理环境中拍摄的任何X射线图像都可以被用作检测骨质疏松症的额外机会，从而减少初级保健提供者识别高危患者并转介他们进行诊断测试的责任。我们的创新有可能使骨质疏松症病例发现率增加20%。这相当于患者预后的5%改善(每年25,000+更少的脆性骨折)，并为NHS直接节省5%的成本(GB 2.2亿+每年)。通过利用现有的图像数据，这种方法不需要在护理环境中进行额外的资本投资，而且对患者来说是非侵入性的。

Osteoporosis is characterised by a degradation of the bone mass and an alteration of the bone microarchitecture. It is estimated that more than 200M people are suffering from osteoporosis worldwide and this number is expected to increase steadily due to changing demographics. If untreated, osteoporosis can lead to fractures which are not only disabling for the patient but cost care systems millions each year. A staggering 1 in 3 women over the age of 50 years and 1 in 5 men will experience osteoporotic fractures in their lifetime, therefore it is imperative that healthcare systems are poised to identify loss of bone mass as early as possible in order to reduce the incidence of fractures.One reason that fractures are so common is that ~75%(2) of osteoporosis patients remain undiagnosed (due to a lack of screening opportunities) and therefore miss the opportunity to receive treatment to lower their risk of fracture. This growing public health burden from osteoporosis and fragility fractures highlights the need to prioritise osteoporosis in healthcare systems immediately. As osteoporosis is highly preventable, a focus on effective management offers an opportunity for substantial savings in the future.In order to reduce the healthcare burden of osteoporosis and minimise the risks of fractures, patients need to be identified earlier so that they can receive effective treatment and interventions. Our proposed solution, OsteoSight, is a machine learning (AI) model that will take an X-ray image, analyse the bone structure within the image and label the bone as either 'osteoporosis' or 'no osteoporosis'. This approach means that any X-ray image taken in the secondary care setting can be leveraged as an additional opportunity to detect osteoporosis, reducing the onus on primary care providers to identify at-risk patients and refer them for diagnostic testing.Our innovation has the potential to increase osteoporosis case-finding by as much as 20%. This equates to a 5% improvement in patient outcomes (25,000+ fewer fragility fractures per year) and a 5% direct cost saving for the NHS (£220+ million per year). By leveraging existing image data, this approach requires no additional capital investments within the care settings and is non-invasive for the patients.