Introduction Quantum Convolutional Neural Network (QCNN)-Long Short-Term Memory (LSTM) models were studied to provide sequential relationships for each timepoint in MRIs of patients with Multiple Sclerosis (MS). In this pilot study, we compared three QCNN-LSTM models for binary classification of MS disability benchmarked against classical neural network architectures. Our hypothesis is that quantum models will provide competitive performance. Methods Matrix Product State (MPS), reverse Multistate Entanglement Renormalization Ansatz (MERA), and Tree-Tensor Network (TTN) circuits were paired with LSTM layer to process near-annual MRI data of patients diagnosed with MS. These were benchmarked against a Visual Geometry Group (VGG)-LSTM and a Video Vision Transformer (ViViT). Predicted logits were measured against ground truth labels of each patient's Extended Disability Severity Score (EDSS) using binary cross-entropy loss. Training/validation/holdout testing was partitioned using 5-fold cross validation with a total split of 60:20:20. Levene's test of variance was used to measure statistical difference and Student's t-test for paired model differences in mean. Results The MPS-LSTM, reverse MERA-LSTM, and TTN-LSTM had holdout testing ROC-AUC of 0.70, 0.77, and 0.81, respectively (p-value 0.915). VGG16-LSTM and ViViT performed similarly with ROC-AUC of 0.73 and 0.77, respectively (p-value 0.631). Overall variance and mean were not statistically significant (p-value 0.713), however, time to train was significantly faster for the QCNN-LSTMs (39.4 sec per fold vs. 224 and 218, respectively, p-value <0.001). Conclusion QCNN-LSTM models perform competitively to their classical counterparts with greater efficiency in train time. Clinically, these can add value in terms of efficiency to time-dependent deep learning prediction of disease progression based upon medical imaging.

翻译：引言 研究量子卷积神经网络（QCNN）-长短期记忆（LSTM）模型，以提供多发性硬化症（MS）患者各时间点MRI的序列关联。在本初步研究中，我们比较了三种QCNN-LSTM模型在MS残疾二分类任务中的表现，并与经典神经网络架构进行基准对照。我们的假设是量子模型将展现具有竞争力的性能。方法 将矩阵乘积态（MPS）、逆向多态纠缠重整化群（MERA）和树张量网络（TTN）电路与LSTM层结合，处理确诊MS患者的近年度MRI数据。这些模型与视觉几何组（VGG）-LSTM及视频视觉变换器（ViViT）进行对比。预测logits通过二元交叉熵损失与每位患者的扩展残疾严重度评分（EDSS）真实标签进行度量。训练/验证/保留测试采用5折交叉验证划分，总体比例为60:20:20。方差齐性采用Levene检验，模型配对均值差异采用Student t检验。结果 MPS-LSTM、逆向MERA-LSTM和TTN-LSTM的保留测试ROC-AUC分别为0.70、0.77和0.81（p值0.915）。VGG16-LSTM和ViViT的性能相近，ROC-AUC分别为0.73和0.77（p值0.631）。总体方差和均值无统计学显著性差异（p值0.713），但QCNN-LSTM的训练时间显著更快（每折39.4秒对比224秒和218秒，p值<0.001）。结论 QCNN-LSTM模型在训练效率上显著优于经典模型，同时保持竞争性性能。在临床应用中，这些模型可基于医学影像为时间依赖性深度学习疾病进展预测提供效率提升价值。