研究发现 AI 模型可以识别有自闭症风险的幼儿，准确率达 80%

最近的研究表明，人工智能 (AI) 在协助识别有自闭症风险的幼儿方面具有潜力，对于两岁以下儿童的准确率约为 80%。

Artificial intelligence (AI) has shown promise in aiding the identification of toddlers at risk of autism, with an accuracy rate of about 80% for children under two, according to recent research.

最近的研究表明，人工智能 (AI) 在帮助识别有自闭症风险的幼儿方面显示出良好的前景，对于两岁以下儿童的准确率约为 80%。

A team of researchers from the Karolinska Institutet in Sweden developed a machine learning-based screening system. While the AI model cannot replace traditional diagnostic methods, it could help identify children early on who may need further clinical evaluation.

瑞典卡罗林斯卡学院的一组研究人员开发了一种基于机器学习的筛查系统。虽然人工智能模型无法取代传统的诊断方法，但它可以帮助尽早识别可能需要进一步临床评估的儿童。

“Using [the] AI model, it can be possible to use available information and identify individuals with an elevated likelihood for autism so that they can get earlier diagnosis and help,” said Dr. Kristiina Tammimies, a study co-author.

研究合著者克里斯蒂娜·塔米米斯 (Kristiina Tammimies) 博士表示：“使用人工智能模型，可以利用现有信息并识别自闭症可能性较高的个体，以便他们能够得到更早的诊断和帮助。”

However, she cautioned that the model should not be viewed as a standalone diagnostic tool, reiterating that the final diagnosis should be conducted through standard clinical methods.

然而，她警告说，该模型不应被视为独立的诊断工具，并重申最终诊断应通过标准临床方法进行。

The AI model was developed using data from the U.S.-based Spark study, which provided information on 15,330 children diagnosed with autism and an equal number of children without the condition.

该人工智能模型是利用美国 Spark 研究的数据开发的，该研究提供了 15,330 名被诊断患有自闭症的儿童和同等数量的非自闭症儿童的信息。

From medical and background questionnaires, the researchers selected 28 measures that could be easily obtained before children reach 24 months of age, such as age at first smile, eating behaviors and age at first construction of longer sentences.

研究人员从医学和背景调查问卷中选择了28项在儿童24个月大之前可以轻松获得的指标，例如第一次微笑的年龄、饮食行为和第一次构造较长句子的年龄。

Using machine learning to analyze patterns in the data, the research team compared the identified patterns between autistic and non-autistic children to build four different models, selecting the most effective one for further testing.

研究团队利用机器学习分析数据中的模式，比较了自闭症和非自闭症儿童之间已识别的模式，建立了四种不同的模型，选择最有效的模型进行进一步测试。

When applied to a separate dataset of 11,936 participants, the model correctly identified 78.9% of the children as either autistic or non-autistic. Specifically, the accuracy was 78.5% for children aged up to two years, 84.2% for those aged two to four years and 79.2% for those aged four to ten years.

当应用于包含 11,936 名参与者的单独数据集时，该模型正确识别出 78.9% 的儿童为自闭症或非自闭症。具体来说，两岁以下儿童的准确率为78.5%，两岁至四岁儿童的准确率为84.2%，四岁至十岁儿童的准确率为79.2%。

An additional test using a dataset of 2,854 autistic individuals resulted in a lower accuracy rate of 68%, which the researchers attributed to differences in the dataset, including some missing parameters.

使用 2,854 名自闭症患者的数据集进行的另一项测试得出的准确率较低为 68%，研究人员将其归因于数据集的差异，包括一些缺失的参数。

The study identified several key measures that significantly influenced the AI model's prediction of autism, including problems with eating certain foods, the age at which a child first constructed longer sentences, the age at which a child achieved potty training and the age at which a child first smiled.

该研究确定了几个对人工智能模型对自闭症预测有显着影响的关键指标，包括吃某些食物的问题、孩子第一次构造较长句子的年龄、孩子进行如厕训练的年龄以及孩子开始进行如厕训练的年龄。首先微笑。

These factors, according to the research team, played a crucial role in the model’s ability to differentiate between autistic and non-autistic children.

研究小组表示，这些因素在模型区分自闭症和非自闭症儿童的能力中发挥了至关重要的作用。

Further analysis revealed that the model tended to identify autism more accurately in individuals who exhibited more severe symptoms and broader developmental issues. This finding suggests that the model might be more effective at recognizing cases with more noticeable developmental challenges accompanying autism.

进一步的分析表明，该模型倾向于更准确地识别表现出更严重症状和更广泛发育问题的个体的自闭症。这一发现表明，该模型可能更有效地识别自闭症伴随的更明显的发育挑战的病例。

Despite the promising results, some experts expressed concerns about the model's ability to correctly identify non-autistic children. With an 80% accuracy rate, the model could potentially lead to overdiagnosis and unnecessary stress for families, as 20% of non-autistic children might be incorrectly flagged as possibly autistic.

尽管结果令人鼓舞，但一些专家对该模型正确识别非自闭症儿童的能力表示担忧。该模型的准确率高达 80%，可能会导致过度诊断并给家庭带来不必要的压力，因为 20% 的非自闭症儿童可能被错误地标记为可能患有自闭症。

Professor Ginny Russell from the University of Exeter sounded a note of caution regarding the push for early diagnosis, especially in very young children.

埃克塞特大学的金妮·拉塞尔教授对推动早期诊断，特别是对于年幼的儿童，发出了警告。

“It can be hard to tell the difference between a toddler who has a severe impairment and one who is simply developing more slowly but will eventually ‘catch up.’ I would not recommend applying psychiatric labels to children under the age of two on the basis of a limited range of behavioral indicators, such as whether they eat certain foods,” Russell said.

“很难区分患有严重障碍的幼儿和只是发育较慢但最终会‘赶上来’的幼儿。我不建议根据有限的行为指标（例如是否吃某些食物）对两岁以下的儿童贴上精神病学标签，”拉塞尔说。