Innovative Use of fungalAi for Antifungal Stewardship in Haematology-oncology Patients
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- Accuracy of electronic surveillance using fungalAi natural language processing compared to active manual methods for detection of fungal pneumonia
研究概览
简要总结
This national Australian study will validate and implement an effective approach to real-time electronic surveillance of fungal infections in patients with blood cancers using technology based on artificial intelligence. It will establish metrics for antifungal stewardship allowing benchmarking of these programs; provide decision support for radiologist interpretation of chest imaging and improve reporting, audit and feedback practices in hospitals where these infections are managed.
详细描述
Invasive fungal diseases (IFD) are rare infections that cause a life-threatening pneumonia in patients with weakened immune systems usually due to cancer chemotherapy and transplantation. Fungal spores are found in air, water and soil making exposure unavoidable in vulnerable patients. In developed countries, molds like Aspergillus are the most challenging type of IFD to diagnose and treat. These infections usually manifest as a culture-negative fungal pneumonia and account for approximately 300K of the 1.9M cases of IFD globally, but estimates are not accurate due to an absence of surveillance systems in hospitals where these infections are managed. Hospitals spend millions on antifungal drugs but are unaware of their patients affected, the effectiveness of their prevention efforts and hospital outbreaks may go unnoticed because surveillance, audit and feedback of fungal infections is not occurring.
Optimising patient outcomes through timely diagnosis and appropriate prescribing of antifungal drugs is the goal of antifungal stewardship programs. Antifungal stewardship is of growing importance to hospitals world-wide because antifungal drugs are few in number, expensive to use and are associated with significant side-effects and drug interactions. Surveillance, audit and feedback are the cornerstones of antifungal stewardship programs that ensure patient care is meeting high standards. However, currently hospitals do not have the mechanisms to detect rare events like fungal infections because it usually presents as a pneumonia buried among hundreds of imaging scans.
"fungalAi™" (fungalAi.com) is a technology based on artificial intelligence (Ai) that uses existing data in hospitals to make real time surveillance of fungal infections possible and assist radiologist interpretation of diagnostic imaging. fungalAi does this through:
- Natural language processing, a computational method of understanding human language.
- Deep learning based image analysis of diagnostic imaging and
- An expert system that integrates clinical data.
What will be the impact?
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Adults and children
- •Under the haematology service at participating sites
- •Inpatient and ambulatory patients.
排除标准
- •No exclusion criteria
结局指标
主要结局
Accuracy of electronic surveillance using fungalAi natural language processing compared to active manual methods for detection of fungal pneumonia
时间窗: 12 months
Sensitivity, specificity, ROC, Area under precision-recall curve of Ai assisted surveillance for fungal pneumonia using natural language processing of imaging reports compared to active manual surveillance
次要结局
- Accuracy of feature detection of fungal pneumonia using deep learning based image analysis of chest CT compared to radiologist expertise.(12 months)
- Accuracy of disease classification of deep learning based image analysis for fungal pneumonia at scan level.(12 months)
- Accuracy of disease classification of an expert system integrating microbiology and antifungal drug prescriptions with text and image analysis compared to active manual surveillance.(12 months)