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Machine learning algorithms revealed distorted retinal vascular branching in individuals with bipolar disorder
Journal of Affective Disorders ( IF 6.6 ) Pub Date : 2022-07-27 , DOI: 10.1016/j.jad.2022.07.060
Murat Ilhan Atagun 1 , Guray Sonugur 2 , Aygun Yusifova 3 , Ibrahim Celik 2 , Nagihan Ugurlu 4
Affiliation  

Background

Converging evidence designate vascular vulnerability in bipolar disorder. The predisposition progresses into distortion in time, thus detection of the vascular susceptibility may help reducing morbidity and mortality. It was aimed to assess retinal fundus vasculature in cardiovascular risk-free patients with bipolar disorder.

Methods

Total of 68 individuals (38 patients with bipolar disorder, 30 healthy controls) were enrolled. In order to avoid from degenerative processes, participants were between 18 and 45 years of age, vascular risk factors were eliminated. Microscopic retinal fundus images were processed with machine learning algorithms (multilayer perceptron and support vector machine) and artificial neural network approaches.

Results

In comparison to the healthy control group, the bipolar disorder group had lower number of breaking points (P < 0.001), lower number of curved vessel segments (P < 0.001). Total length of smooth vessels was longer (P = 0.040), and total length of curved vessel segments was significantly shorter (P < 0.001) than the control group. Vascular endothelial growth factor levels and gender were the confounders. There were significant correlations between vascular measures and serum lipid levels.

Limitations

Sample size was small and patients were on various medications.

Conclusions

These results indicate distortion in retinal vascular branching in bipolar disorder. Disrupted branching may reflect disturbed prosperity of retinal vascular plexus in patients with bipolar disorder. Alterations in the retinal vessels might be indicators of disruption in cerebral vascular system efficiency and thus neurovascular unit dysfunction in bipolar disorder.



中文翻译:

机器学习算法揭示了双相情感障碍患者的视网膜血管分支扭曲

背景

趋同的证据表明双相情感障碍的血管易损性。易感性随着时间的推移发展为扭曲,因此检测血管易感性可能有助于降低发病率和死亡率。该研究旨在评估无心血管风险的双相情感障碍患者的视网膜眼底脉管系统。

方法

共招募了 68 名个体(38 名双相情感障碍患者,30 名健康对照者)。为了避免退化过程,参与者年龄在 18 至 45 岁之间,血管危险因素被消除。用机器学习算法(多层感知器和支持向量机)和人工神经网络方法处理显微视网膜眼底图像。

结果

与健康对照组相比,双相情感障碍组的断裂点数量较少(P  < 0.001),弯曲血管段数量较少(P  < 0.001)。平滑血管总长度比对照组长(P  =0.040),弯曲血管段总长度显着短于对照组(P  <0.001)。血管内皮生长因子水平和性别是混杂因素。血管测量值与血脂水平之间存在显着相关性。

限制_

样本量小,患者服用各种药物。

结论

这些结果表明双相情感障碍中视网膜血管分支的扭曲。中断的分支可能反映双相情感障碍患者视网膜血管丛的繁荣受到干扰。视网膜血管的改变可能是脑血管系统效率中断的指标,因此是双相情感障碍中神经血管单元功能障碍的指标。

更新日期:2022-07-27
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