The Institute for Rare Diseases, The Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Tel-Hashomer, Israel; The Talpiot Medical Leadership Program, Sheba Medical Center, Tel-Hashomer, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
The Danek Gertner Institute of Human Genetics, Sheba Medical Center, Tel-Hashomer, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
Eur J Med Genet. 2020 Jul;63(7):103927. doi: 10.1016/j.ejmg.2020.103927. Epub 2020 Apr 13.
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.
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.
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).
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.
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.
黏脂贮积症 IV 型（ML-IV）是一种罕见的常染色体隐性溶酶体贮积病，由 MCOLN1 基因突变引起。ML-IV 的表现为发育迟缓、内斜视和角膜混浊。虽然临床表型已有详细描述，但 ML-IV 的诊断通常具有挑战性且难以捉摸。
我们在 ML-IV 患者的临床观察中发现，他们具有共同且可识别的面部特征，这在目前的文献中尚未描述。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。
使用 DeepGestalt 算法，对 10 名 ML-IV 患者的 50 张二维面部图像进行了分析，并与未受影响的对照组（n=98）和受其他遗传疾病影响的个体（n=99）进行了比较。结果以受试者工作特征曲线（ROC）的曲线下面积（AUC）表示。
与未受影响的病例和诊断为 ML-IV 以外综合征的病例相比，ML-IV 组的 AUC 分别为 0.822（p 值<0.01）和 0.885（p 值<0.001）。
我们描述了 ML-IV 患者特有的可识别面部特征。DeepGestalt 技术再次证实了这种常见的面部表型，为临床医生提供了额外的工具，可能有助于更早地诊断这种罕见且诊断不足的疾病。
