Deep-UV Microscopy for Real-Time Adequacy Analysis of Bone Marrow Aspirates

用于骨髓抽吸物实时充分性分析的深紫外显微镜

• 批准号: 10761397
• 负责人: Francisco E Robles
• 金额: $ 27.58万
• 依托单位: CELLIA SCIENCE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10761397
• 关键词: Adopted; Adoption; Algorithms; Aplastic Anemia; Assessment tool; Bone Marrow Aspiration; Characteristics; Classification; Clinical; Collection; Complement; Computer software; Data; Detection; Devices; Diagnosis; Engineering; Ensure; Eye; Feedback; Giemsa stain; Goals; Health Personnel; Hematology; Hemodilution; Image; Label; Left; Light; Machine Learning; Malignant Neoplasms; Manuals; Microscopy; Monitor; Neoplasm Metastasis; Nurses; Optics; Pathology; Patient Care; Patient-Focused Outcomes; Physician Assistants; Procedures; Process; Protocols documentation; Quality of Care; Sampling; Science; Sensitivity and Specificity; Sickle Cell Anemia; Slide; Solid Neoplasm; Specificity; Staging; Stains; Techniques; Technology; Time; Training; Visual; Visualization; Work; aspirate; attenuation; bone; cellular imaging; feature extraction; improved; innovation; instrument; leukemia; ophthalmic examination; point of care; prototype; screening; success; time use; ultraviolet

Project Summary/Abstract Bone marrow aspirates are critical to the diagnosis, staging, and monitoring of hematologic conditions and cancers (e.g., leukemia, aplastic anemia, sickle cell disease, and metastasis of solid tumors), but 8-50% of aspirations are unsuccessful due to operator technique, hemodilution, or underlying pathology. Because this process is manual and error-prone, there is an opportunity to improve patient outcomes by providing real-time and automated feedback on the sample quality. Cellia Science will enable improved quality and reliability of bone marrow aspiration procedures by developing a point-of-care screening instrument. Our approach is based on a recently developed label-free, deep-ultraviolet (UV) technique for cell imaging and analysis. Preliminary data has shown that the spicules present in a bone marrow aspirate are easily identifiable by their characteristic deep blue hue in the unstained pseudocolorized UV image—a result of strong light attenuation at 255nm by bone spicules. The resulting deep-UV images can be generated in under 3 minutes, making the technique suitable for real-time use during aspiration procedures, and are nearly identical to the Giemsa- stained slides, which take over 45 min to process. Label-free deep-UV imaging can be combined with machine learning techniques for feature extraction and classification, which will enable automated quality assessment of aspirate smears without a pathology technician. We will leverage this technology to develop a bone marrow aspirate quality screening device for use during aspiration procedures. Towards this goal, we propose quantify sensitivity and specificity of spicule detection by deep-UV microscopy and evaluate concordance of deep-UV aspirate assessment with visual assessment by trained technician, with Giemsa-stained samples serving as the ground truth. We will also develop prototype instrument for automated spicule adequacy assessment to enable adoption of this technique without a specially trained pathology technician. To automate the adequacy assessment, we will use machine learning techniques for feature extraction and classification, detect the presence of one or more spicules in the sample. Successful implementation of this device is expected to increase the fraction of successful procedures, which will drastically improve the quality of care for the patient.

项目概要/摘要 骨髓穿刺对于血液学病症的诊断、分期和监测至关重要。 癌症（例如白血病、再生障碍性贫血、镰状细胞病和实体瘤转移），但 8-50% 由于操作者技术、血液稀释或潜在病理原因，抽吸不成功。 过程是手动的且容易出错，有机会通过提供实时信息来改善患者的治疗结果 Ceria Science 的样品质量自动反馈将提高样品的质量和可靠性。 我们的方法是通过开发即时筛查仪器来进行骨髓抽吸手术。 基于最近开发的用于细胞成像和分析的无标记深紫外线 (UV) 技术。 初步数据表明，骨髓抽吸物中存在的骨针很容易通过其特征来识别。 未染色的伪彩色 UV 图像中特有的深蓝色色调——这是强烈光衰减的结果 255nm 的骨针可在 3 分钟内生成深紫外图像，从而使 该技术适合在抽吸过程中实时使用，并且与 Giemsa 几乎相同 染色载玻片，无需标记的深紫外成像可与机器结合使用，处理时间超过 45 分钟。 特征提取和分类的学习技术，这将使自动质量评估成为可能 我们将利用这项技术来开发骨髓。 为了实现这一目标，我们建议进行量化。 深紫外显微镜检测针状体的灵敏度和特异性，并评估深紫外显微镜的一致性 由训练有素的技术人员通过目视评估进行抽吸评估，并使用吉姆萨染色的样本作为 我们还将开发用于自动骨针充足性评估的原型仪器。 无需经过专门培训的病理学技术人员即可采用该技术，以实现充分自动化。 评估时，我们将使用机器学习技术进行特征提取和分类，检测 样品中存在一个或多个针状物预计该装置的成功实施。 增加手术成功率，这将大大提高患者的护理质量。