Neural Networks for Classification and Image Generation of Aging in Genetic Syndromes.
Neural Networks for Classification and Image Generation of Aging in Genetic Syndromes.
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DOI：
10.3389/fgene.2022.864092
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发表时间：
2022
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
背景：在医学遗传学中，神经网络的一个应用是基于患者面部图像的遗传病诊断。虽然这些应用已经在主要是儿科受试者的文献中得到了验证，但尚不清楚这些应用是否能够准确地诊断患者的整个生命周期。我们的目标是扩展以前的工作，以确定年龄是否在面部诊断中起作用，以及探索可能有助于总体诊断准确性的其他因素。方法：我们选择了两种相对常见的疾病，Williams综合征和22q11.2缺失综合征。我们建立了一个神经网络分类器，对不同年龄的患病和未患病个体的图像进行训练，并将分类器的准确性与临床遗传学家进行比较。我们分析了显著图的结果，并使用生成性对抗网络来提高准确性。结果：我们的分类器在识别每个年龄组内这两种情况的人脸图像方面优于临床遗传学家(不同年龄组的表现有所不同)：1)2岁以下，2)2-9岁，3)10-19岁，4)20-34岁，5)≥35岁。与临床遗传学家相比，我们的分类器对Williams综合征和22q11.2缺失综合征的总体准确率分别提高了15.5%和22.7%。此外，显著图的比较显示，神经网络学习的关键面部特征随着年龄的不同而不同。最后，对由产生式对抗网络生成的多种不同类型的虚假图像进行联合训练，分类准确率提高了3.25%。结论：临床遗传学家诊断这些疾病的能力受患者年龄的影响。深度学习技术，如我们的分类器，可以根据面部特征更准确地识别整个生命周期的患者。计算机视觉的显著图显示，综合征面部特征属性随着患者年龄的变化而变化。当对真实和虚假图像进行联合训练时，可以观察到分类器准确率略有提高。我们的发现突显了在面部诊断中更多地关注年龄作为混淆因素的必要性。
Background: In medical genetics, one application of neural networks is the diagnosis of genetic diseases based on images of patient faces. While these applications have been validated in the literature with primarily pediatric subjects, it is not known whether these applications can accurately diagnose patients across a lifespan. We aimed to extend previous works to determine whether age plays a factor in facial diagnosis as well as to explore other factors that may contribute to the overall diagnostic accuracy. Methods: To investigate this, we chose two relatively common conditions, Williams syndrome and 22q11.2 deletion syndrome. We built a neural network classifier trained on images of affected and unaffected individuals of different ages and compared classifier accuracy to clinical geneticists. We analyzed the results of saliency maps and the use of generative adversarial networks to boost accuracy. Results: Our classifier outperformed clinical geneticists at recognizing face images of these two conditions within each of the age groups (the performance varied between the age groups): 1) under 2 years old, 2) 2–9 years old, 3) 10–19 years old, 4) 20–34 years old, and 5) ≥35 years old. The overall accuracy improvement by our classifier over the clinical geneticists was 15.5 and 22.7% for Williams syndrome and 22q11.2 deletion syndrome, respectively. Additionally, comparison of saliency maps revealed that key facial features learned by the neural network differed with respect to age. Finally, joint training real images with multiple different types of fake images created by a generative adversarial network showed up to 3.25% accuracy gain in classification accuracy. Conclusion: The ability of clinical geneticists to diagnose these conditions is influenced by the age of the patient. Deep learning technologies such as our classifier can more accurately identify patients across the lifespan based on facial features. Saliency maps of computer vision reveal that the syndromic facial feature attributes change with the age of the patient. Modest improvements in the classifier accuracy were observed when joint training was carried out with both real and fake images. Our findings highlight the need for a greater focus on age as a confounder in facial diagnosis.
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DOI：
10.1002/ajmg.c.31874
发表时间：
2021-03
期刊：
American journal of medical genetics. Part C, Seminars in medical genetics
影响因子：
--
作者：
Bick D;Bick SL;Dimmock DP;Fowler TA;Caulfield MJ;Scott RH
通讯作者：
Scott RH
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
2019年美国医学遗传学劳动力：关注临床遗传学。
DOI：
10.1038/s41436-021-01162-5
发表时间：
2021-08
期刊：
Genetics in medicine : official journal of the American College of Medical Genetics
影响因子：
--
作者：
Jenkins BD;Fischer CG;Polito CA;Maiese DR;Keehn AS;Lyon M;Edick MJ;Taylor MRG;Andersson HC;Bodurtha JN;Blitzer MG;Muenke M;Watson MS
通讯作者：
Watson MS
影响因子：
3.9
作者：
Hennekam, Raoul C. M.;Biesecker, Leslie G.
通讯作者：
Biesecker, Leslie G.
影响因子：
30.8
作者：
Porras, Antonio R.;Rosenbaum, Kenneth;Linguraru, Marius George
通讯作者：
Linguraru, Marius George