STTR Phase I: Intelligent Scoring of Inflammatory Skin Disease Progression

STTR 第一期：炎症性皮肤病进展的智能评分

• 批准号: 1843221
• 负责人: Kelsey Gross
• 金额: $ 22.5万
• 依托单位: 3Derm Systems, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1843221&HistoricalAwards=false
• 关键词: STTR; Phase; Intelligent; Scoring; Inflammatory

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.

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响和商业潜力是赋予提供商前所未有的能力，以更低的成本有效地治疗慢性炎症性皮肤病患者。今天，初级保健医生经常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，特别是当患者接受高成本，高接触治疗如生物制剂或程序，如光疗。通过引入这种自动化的严重程度评分系统，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，评估病人的进展，以调整剂量和治疗的必要性。对于制药公司来说，在临床试验期间让非专家进行这些严重性评估的能力减少了昂贵和限制性的人员配置要求。更便宜的试验可以帮助加速新药上市。这项创新将通过以一种新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。这个小企业技术转让（STTR）第一阶段项目将开发一种临床工具，可以评估慢性皮肤炎症的图像，确定其严重程度，并进行最佳治疗。该系统将能够通过最少的训练指导用户完成图像采集过程，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性依赖于一系列的估计和手动加权平均值，这是一个耗时且有偏见的过程。这个I期研究将探索什么样的信息是必要的算法，以确定银屑病的严重程度。这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这样的计算可以执行。该项目的最终目标是一个工作原型，可以始终如一地评分一些牛皮癣病例，并作为一个概念的证明，为原型在未来的工作中的扩展。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。