Analyzing and visualizing morphological features using machine learning techniques and non‐big data: A case study of macaque mandibles
Analyzing and visualizing morphological features using machine learning techniques and non‐big data: A case study of macaque mandibles
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DOI：
10.1002/ajpa.24469
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发表时间：
2022-01
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影响因子：
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通讯作者：
Takashi Morita;Tsuyoshi Ito;H. Koda;Hikaru Wakamori;Takeshi Nishimura
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作者：
Takashi Morita;Tsuyoshi Ito;H. Koda;Hikaru Wakamori;Takeshi Nishimura
目的形态计量学在理解生物变异和形态表型进化方面发挥着重要作用。这种方法通常对数据有严格的要求，例如对受试者进行严格的对齐，而满足这些要求的数据的收集和手动预处理通常非常耗时。人工智能（AI）技术正在发展，它可能会减少这种负担，但它们通常以成功学习的“大数据”为前提，超出了生物学研究中经验上合理的数量。在这里，我们提出了一种基于深度学习的三维数据分析方法。材料和方法我们建立了一种基于深度学习的三维形态数据分析方法，不需要严格的对齐或难以置信的样本量。我们基准的猕猴性别分类的情况下，指的是计算机断层扫描扫描他们的mandible.ResultsThe模型学习从139下颌骨标本的日本猕猴，并成功地推广了学习的分类以前看不见的标本，同一物种，甚至其他物种的猕猴。此外，我们可视化这些特征区域的数据中，该模型在性别分类过程中使用，并表明，他们是一致的人类experts.DiscussionOur分析所使用的标准不需要严格对齐的数据，所以可以有效地使用在以前的研究中收集的数据与不同的焦点/目标。这种人工智能方法可以帮助研究人员发现不同物种和其他生物群体的新形态特征。这个拟议的人工智能系统的实施将提供给其他研究人员进行进一步的调查。
ObjectivesMorphometrics has played essential roles in the comprehension of biological variation and the evolution of morphological phenotypes. This approach usually imposes strict requirements on data, such as rigid alignment of subjects, and the collection and manual preprocessing of data meeting these requirements are often time consuming. Artificial intelligence (AI) technology is developing and it potentially reduces this load, but they usually presuppose the availability of “big data” for successful learning, beyond the empirically plausible amount in biological studies. Here, we propose a deep learning‐based analysis of three‐dimensional data.Materials and MethodsWe built a deep learning‐based analysis of three‐dimensional morphological data that does not require strict alignment or an implausible sample size. We benchmarked the proposed method by case studying sex classification of macaques, referring to computed tomography scans of their mandible.ResultsThe model learned from just 139 mandible specimens of Japanese macaques and successfully generalized the learned classification to previously unseen specimens of the same species and even other species of macaques. Moreover, we visualized those characteristic regions in the data that the model used during sex classification and showed that they were consistent with the criteria used by human experts.DiscussionOur analysis does not require rigidly aligned data, so can effectively use data collected in previous studies with different focus/aims. This proposed AI method can potentially help researchers to discover new morphological features of different species and other biological groups. Implementation of this proposed AI system will be available to other researchers for further investigation.