Background

Mucolipidosis type IV (ML-IV) is a rare autosomal-recessive lysosomal storage disease, caused by mutations in MCOLN1. ML-IV manifests with developmental delay, esotropia and corneal clouding. While the clinical phenotype is well-described, the diagnosis of ML-IV is often challenging and elusive.

Objective

Our experience with ML-IV patients brought to the clinical observation that they share common and identifiable facial features, not yet described in the literature to date. Here, we utilized a computerized facial analysis tool to establish this association.

Methods

Using the DeepGestalt algorithm, 50 two-dimensional facial images of ten ML-IV patients were analyzed, and compared to unaffected controls (n = 98) and to individuals affected with other genetic disorders (n = 99). Results were expressed in terms of the area-under-the-curve (AUC) of the receiver-operating-characteristic curve (ROC).

Results

When compared to unaffected cases and to cases diagnosed with syndromes other than ML-IV, the ML-IV cohort showed an AUC of 0.822 (p value < 0.01) and an AUC of 0.885 (p value < 0.001), respectively.

Conclusions

We describe recognizable facial features typical in patients with ML-IV. Reaffirmed by the DeepGestalt technology, the described common facial phenotype adds to the tools currently available for clinicians and may thus assist in reaching an earlier diagnosis of this rare and underdiagnosed disorder.

中文翻译：

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IV型粘膜脂肪变性患者的面部共有表型：下一代表型证实了临床观察。

背景

IV型粘脂病（ML-IV）是一种罕见的常染色体隐性溶酶体贮积病，由MCOLN1突变引起。ML-IV表现为发育延迟，内斜视和角膜混浊。尽管临床表型已得到很好的描述，但ML-IV的诊断通常具有挑战性且难以捉摸。

目的

我们对ML-IV患者的经验使他们的临床观察表明他们拥有共同且可识别的面部特征，但迄今尚未在文献中描述。在这里，我们利用计算机化的面部分析工具来建立这种关联。

方法

使用DeepGestalt算法，分析了10名ML-IV患者的50张二维面部图像，并将其与未受影响的对照组（n = 98）和患有其他遗传性疾病的个体（n = 99）进行了比较。结果以接收器工作特性曲线（ROC）的曲线下面积（AUC）表示。

结果

与未受影响的病例和诊断为ML-IV以外的其他症状的病例相比，ML-IV队列的AUC分别为0.822（p值<0.01）和AUC为0.885（p值<0.001）。

结论

我们描述了ML-IV患者典型的可识别面部特征。DeepGestalt技术得到证实，所描述的常见面部表型增加了当前可用于临床医生的工具，因此可能有助于更早地诊断出这种罕见且诊断不足的疾病。