Salomon Carmela, Heinz Kelianne, Aronson-Ramos Judith, Wall Dennis P
Cognoa Inc., 2185 Park Blvd, Palo Alto, CA, 94306, USA.
Society of Developmental and Behavioral Pediatrics, Virginia, USA.
Sci Rep. 2025 Aug 12;15(1):29503. doi: 10.1038/s41598-025-15575-8.
Rapidly rising demand for pediatric autism evaluations has outpaced specialist capacity and created a crisis of delayed diagnoses and treatment. Streamlining the diagnostic process could reduce wait times and optimize use of limited specialist resources. Following strong clinical trial results, Canvas Dx, an AI-based diagnostic, was FDA authorized to support accurate diagnosis or rule-out of autism in children 18-72 months with caregiver or healthcare provider concern for developmental delay. To gain insight into real-world device performance, a de-identified aggregate data analysis of the initial 254 Canvas Dx prescriptions fulfilled post-market authorization was conducted to determine: accuracy of autism predictions compared to clinical reference standard diagnosis and prior clinical trial data, key real-world prescriber and patient characteristics, proportion of determinate device outputs (positive or negative for autism) and impact of decision threshold settings on device performance. In this sample of 254 children with a 54.7% autism prevalence rate (29.1% female, average age 39.99 months), Canvas Dx had a NPV of 97.6% (CI- 92.8% -100.0%) and a PPV of 92.4% (CI-87.7%-97.2%). A majority of cases (63.0%) received a determinate result. Sensitivity and specificity of determinate results were 99.1% (CI-97.3%-100.0%) and 81.6% (CI-70.8%-92.5%) respectively. The median age of children who received a positive for autism output was 37.2 months, which is more than 2 years earlier than the current median age of autism diagnosis. No performance differences were noted based on patients' sex. Compared to clinical trial results, real world performance was equivalent for all key metrics, with the exception of the determinate rate and the PPV which were significantly improved in real world performance. Analysis of real-world Canvas Dx data highlights its feasibility and utility in supporting accurate, equitable and early diagnosis or rule out of autism. With medical coverage and broader clinical adoption, innovative solutions such as Canvas Dx can play an important role in helping to address the growing specialist waitlist crisis, ensuring that more children gain access to targeted therapies during the critical window of neurodevelopment where they have the greatest life-changing impact.
对儿科自闭症评估的需求迅速增长，超过了专家的能力，导致诊断和治疗延迟的危机。简化诊断流程可以减少等待时间，并优化有限专家资源的使用。在取得强有力的临床试验结果后，基于人工智能的诊断工具Canvas Dx获得了美国食品药品监督管理局（FDA）的授权，以支持对18至72个月、其照顾者或医疗服务提供者担心发育迟缓的儿童进行自闭症的准确诊断或排除。为了深入了解该设备在现实世界中的性能，我们对上市后授权的最初254份Canvas Dx处方进行了去识别化的汇总数据分析，以确定：与临床参考标准诊断和先前临床试验数据相比，自闭症预测的准确性；现实世界中关键的开处方者和患者特征；确定性设备输出（自闭症阳性或阴性）的比例；以及决策阈值设置对设备性能的影响。在这个包含254名儿童的样本中，自闭症患病率为54.7%（女性占29.1%，平均年龄39.99个月），Canvas Dx的阴性预测值（NPV）为97.6%（置信区间 - 92.8% - 100.0%），阳性预测值（PPV）为92.4%（置信区间 - 87.7% - 97.2%）。大多数病例（63.0%）得到了确定性结果。确定性结果的敏感性和特异性分别为99.1%（置信区间 - 97.3% - 100.0%）和81.6%（置信区间 - 70.8% - 92.5%）。自闭症输出为阳性的儿童的中位年龄为37.2个月，比目前自闭症诊断的中位年龄早两年多。未观察到基于患者性别的性能差异。与临床试验结果相比，除了确定性率和阳性预测值在现实世界中的性能显著提高外，所有关键指标的现实世界性能相当。对Canvas Dx现实世界数据的分析突出了其在支持自闭症准确、公平和早期诊断或排除方面的可行性和实用性。随着医保覆盖和更广泛的临床应用，像Canvas Dx这样的创新解决方案可以在帮助解决日益严重的专家等候名单危机方面发挥重要作用，确保更多儿童在神经发育的关键窗口期获得有针对性的治疗，这一时期对他们的生活改变影响最大。
