STTR Phase I: Intelligent Scoring of Inflammatory Skin Disease Progression

这项小型企业技术转移（STTR）第一阶段项目的广泛影响和商业潜力是赋予供应商前所未有的能力，以更低的成本有效治疗慢性炎症性皮肤病患者。如今，初级保健医生通常会将患有这些疾病的患者转介给皮肤科医生进行初步诊断和多次随访预约，特别是当患者被要求使用生物制剂等高成本、高接触性疗法或光疗等手术时。通过引入这种自动严重程度评分系统，这些提供者将拥有皮肤科医生的专业知识，使他们能够评估疾病的严重程度，并提供以前只能通过专家提供的系统治疗。它还将建立更客观的评估，评估患者的进展，以在必要时调整剂量和治疗。对于制药公司来说，在临床试验期间让非专业人员进行这些严重程度评估的能力减少了昂贵和限制性的人员需求。成本较低的试验有助于加快新药上市。这一创新将通过以一种新颖的方式应用已知的机器学习技术来解决一个困难的视觉问题，从而增强科学和技术的理解。这项小型企业技术转移（STTR）第一阶段项目将开发一种临床工具原型，该工具可以评估慢性皮肤炎症的图像，确定其严重程度，并提出治疗建议。该系统将能够通过最少的训练指导用户完成图像收集过程，确定信息是否足够，并通过新颖的机器学习技术请求额外的信息。目前，临床医生试图评估慢性炎症性皮肤病的严重程度依赖于一系列的估计和人工加权平均，这是一个耗时且有偏见的过程。这个第一阶段的研究将探索什么信息是必要的算法来确定牛皮癣病例的严重程度。它将决定机器是否有可能引导成像仪以更高的细节通过感兴趣的成像区域，而不是整个身体。它还将探讨如何快速和实际地执行这样的计算。该项目的最终目标是一个工作原型，可以持续地对一些牛皮癣病例进行评分，并作为在未来工作中扩展原型的概念证明。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact and commercial potential of this Small Business Technology Transfer(STTR) Phase I project is to empower providers with the unprecedented ability to treat patientswith chronic inflammatory skin disease effectively and at a lower cost. Today, primary carephysicians often refer patients with these conditions to dermatologists for the initial diagnosisand multiple follow-up appointments, especially when patients are prescribed high cost, hightouch therapies like biologics or procedures, such as phototherapy. By introducing thisautomated severity scoring system, these providers will have the expertise of dermatologists attheir fingertips, giving them the ability to assess disease severity and provide systemictreatments previously available only through specialists. It will also establish more objectiveassessments, evaluating patient progress to adjust dosage and treatments as necessary. Forpharmaceutical companies, the ability to have non-specialists make these severity assessmentsduring clinical trials reduces expensive and restrictive staffing requirements. Less expensive trials canhelp expedite new drugs to market. This innovation will enhance scientific and technologicalunderstanding by applying known machine learning techniques in a novel manner to solve adifficult visual problem.This Small Business Technology Transfer (STTR) Phase I project will prototype a clinical toolthat can assess images of a chronic skin inflammation, determine its severity, and suggesttreatment. This system will be able to guide users with minimal training through the imagecollection process, determine if the information is sufficient, and request additional information ifrequired via novel machine learning techniques. Currently, clinicians trying to assess theseverity of chronic inflammatory skin diseases rely on a series of estimations and manualweighted averages, a time consuming and biased process. This Phase I research will explorewhat information is necessary for an algorithm to determine the severity of a psoriasis case. Itwill determine if it is possible for a machine to guide an imager through imaging regions ofinterest at higher detail, rather than the entire body at large. It will also explore how quickly andpractically such a calculation can be performed. The end goal of the project is a workingprototype that can consistently score a few psoriasis cases and serve as a proof of concept forexpansion of the prototype in future work.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.