新研究强调了MIH人工智能诊断的最新进展-动脉网

New study highlights latest advances in AI diagnosis of MIH

Dental Tribune 等信源发布 2024-09-23 18:41

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Dental Tribune International

国际牙科论坛

Mon. 23. September 2024

周一。2024年9月23日

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MUNICH, Germany: Molar incisor hypomineralisation (MIH) constitutes a global oral health issue, affecting around 14% of the world’s population. Like with other areas of oral health, the detection of MIH is being revolutionised through the utilisation of artificial intelligence (AI) models. Building on a groundbreaking study that introduced an open-access AI model for the detection of MIH using digital photographs, a new study by a group of researchers based at the Department of Conservative Dentistry and Periodontology at LMU Munich has externally validated the model, thereby bolstering confidence in its clinical efficacy..

德国慕尼黑：磨牙切牙低矿化（MIH）构成了全球口腔健康问题，影响了全球约14%的人口。与口腔健康的其他领域一样，通过利用人工智能（AI）模型，MIH的检测正在发生革命性的变化。基于一项开创性的研究，该研究引入了一种开放获取的人工智能模型，用于使用数码照片检测MIH，慕尼黑大学保守牙科和牙周病学系的一组研究人员进行的一项新研究对该模型进行了外部验证，从而增强了对其临床疗效的信心。。

The model was first developed on the basis of 18,719 photographs of teeth with 34,710 pathological findings. The images were then analysed by trained dentists according to internationally accepted classification frameworks. As described in the initial paper, the model yielded remarkable high internal validity, but the authors cautiously emphasised that the model needed to be further improved and externally validated..

该模型首先基于18719张牙齿照片和34710个病理结果开发。然后由训练有素的牙医根据国际公认的分类框架对图像进行分析。如最初的论文所述，该模型产生了显着的高内部效度，但作者谨慎地强调，该模型需要进一步改进和外部验证。。

Taking this lead, the validating researchers tested the model using images freely accessible from internet searches. A total of 455 images were evaluated by a working group of five dentists, and they determined that 277 images showed teeth with MIH on occlusal or smooth surfaces and 178 images showed teeth without MIH.

以此为先导，验证研究人员使用可从互联网搜索免费获得的图像测试了该模型。由五名牙医组成的工作组共评估了455张图像，他们确定277张图像显示咬合或光滑表面上有MIH的牙齿，178张图像显示没有MIH的牙齿。

When these images were subsequently subjected to analysis by the AI model, the results were highly illuminating: the model achieved an overall accuracy of 94.3% for image-based detection of MIH..

当这些图像随后通过AI模型进行分析时，结果非常有启发性：该模型对基于图像的MIH检测的总体准确率达到94.3%。。

Speaking to Dental Tribune International, co-author Prof. Jan Kühnisch, head of the section of paediatric dentistry at the department, commented on the significance of the study. “Dental photographs—which have to be understood as the digital and machine-readable equivalent to the clinical examination—can be automatically evaluated by AI methods and may potentially contribute to accurate diagnostic evaluations in the future.

在接受《国际牙科论坛报》采访时，该系儿科牙科科科长、合著者Jan Kühnisch教授评论了这项研究的重要性。“牙科照片必须被理解为与临床检查相当的数字和机器可读的照片，可以通过人工智能方法自动评估，并可能有助于未来准确的诊断评估。

When considering that images can be captured by various intra-oral cameras, semi-professional cameras or even mobile phones, it’s a small step to be analysed by AI algorithms and receive a fast and potentially cheap dental diagnosis.”.

当考虑到可以通过各种口内摄像机，半专业摄像机甚至手机捕获图像时，通过人工智能算法进行分析并获得快速且潜在廉价的牙科诊断是一小步。”。

This naturally raises the question as to how such AI-assisted diagnosis stands to transform the dental profession. Prof. Kühnisch continued, “I think that such AI models will be integrated in dental software apps step by step and will support professional work. It’s similar to other dental developments, such as self-etching adhesive.

这自然提出了一个问题，即这种人工智能辅助诊断如何改变牙科专业。Kühnisch教授继续说：“我认为这种人工智能模型将逐步集成到牙科软件应用程序中，并支持专业工作。它类似于其他牙科开发，例如自蚀刻粘合剂。

There was a vision long ago, but it took one or two decades to make the performance sufficiently stable so that it could be used in daily dental practice.”.

很久以前就有一个愿景，但需要一到二十年才能使性能足够稳定，以便可以在日常牙科实践中使用。”。

Like with all other areas of dentistry being reconfigured through the increased penetration of AI, the bright line test will remain how scientifically accurate these models are and how they can be integrated meaningfully alongside human evaluations and judgements.

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The study, titled “External validation of an artificial intelligence-based method for the detection and classification of molar incisor hypomineralisation in dental photographs”, was published in the September 2024 issue of the Journal of Dentistry.

这项研究题为“基于人工智能的方法的外部验证，用于检测和分类牙科照片中的磨牙门牙低矿化”，发表在2024年9月的《牙科杂志》上。

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