Glaucoma is a chronic eye disease characterized by optic neuropathy, leading
to irreversible vision loss. It progresses gradually, often remaining
undiagnosed until advanced stages. Early detection is crucial to monitor
atrophy and develop treatment strategies to prevent further vision impairment.
Data-centric methods have enabled computer-aided algorithms for precise
glaucoma diagnosis.
In this study, we use deep learning models to identify complex disease traits
and progression criteria, detecting subtle changes indicative of glaucoma. We
explore the structure-function relationship in glaucoma progression and predict
functional impairment from structural eye deterioration. We analyze statistical
and machine learning methods, including deep learning techniques with optical
coherence tomography (OCT) scans for accurate progression prediction.
Addressing challenges like age variability, data imbalances, and noisy
labels, we develop novel semi-supervised time-series algorithms:
1. Weakly-Supervised Time-Series Learning: We create a CNN-LSTM model to
encode spatiotemporal features from OCT scans. This approach uses age-related
progression and positive-unlabeled data to establish robust pseudo-progression
criteria, bypassing gold-standard labels.
2. Semi-Supervised Time-Series Learning: Using labels from Guided Progression
Analysis (GPA) in a contrastive learning scheme, the CNN-LSTM architecture
learns from potentially mislabeled data to improve prediction accuracy.
Our methods outperform conventional and state-of-the-art techniques.

利用深度学习模型，该研究探索了青光眼的结构功能关系，从结构性眼部退化中预测功能受损，并且开发出半监督时间序列算法以准确预测青光眼的进展。

利用眼部结构变化预测青光眼进展的深度学习

Deep Learning to Predict Glaucoma Progression using Structural Changes  in the Eye

Knee Osteoarthritis (KOA) is a highly prevalent chronic musculoskeletal
condition with no currently available treatment. The manifestation of KOA is
heterogeneous and prediction of its progression is challenging. Current
literature suggests that the use of multi-modal data and advanced modeling
methods, such as the ones based on Deep Learning, has promise in tackling this
challenge. To date, however, the evidence on the efficacy of this approach is
limited. In this study, we leveraged recent advances in Deep Learning and,
using a Transformer approach, developed a unified framework for the multi-modal
fusion of knee imaging data. Subsequently, we analyzed its performance across a
range of scenarios by investigating multiple progression horizons -- from
short-term to long-term. We report our findings using a large cohort
(n=2421-3967) derived from the Osteoarthritis Initiative dataset. We show that
structural knee MRI allows identifying radiographic KOA progressors on par with
multi-modal fusion approaches, achieving an area under the ROC curve (ROC AUC)
of 0.70-0.76 and Average Precision (AP) of 0.15-0.54 in 2-8 year horizons.
Progression within 1 year was better predicted with a multi-modal method using
X-ray, structural, and compositional MR images -- ROC AUC of 0.76(0.04), AP of
0.13(0.04) -- or via clinical data. Our follow-up analysis generally shows that
prediction from the imaging data is more accurate for post-traumatic subjects,
and we further investigate which subject subgroups may benefit the most. The
present study provides novel insights into multi-modal imaging of KOA and
brings a unified data-driven framework for studying its progression in an
end-to-end manner, providing new tools for the design of more efficient
clinical trials. The source code of our framework and the pre-trained models
are made publicly available.

本研究通过使用深度学习的 Transformer 方法开发了一个多模态膝关节成像数据融合的统一框架，研究了该方法在 Knee Osteoarthritis 进展预测中的表现，并发现 MRI 可以与多模态融合方法媲美，对于短期至长期不同进展范围，ROC 曲线下面积为 0.70-0.76，平均精确度在 0.15-0.54 之间。

多模态 Transformers 全流程预测膝骨关节炎进展

End-To-End Prediction of Knee Osteoarthritis Progression With  Multi-Modal Transformers

Brain structural MRI has been widely used to assess the future progression of
cognitive impairment (CI). Previous learning-based studies usually suffer from
the issue of small-sized labeled training data, while there exist a huge amount
of structural MRIs in large-scale public databases. Intuitively, brain
anatomical structures derived from these public MRIs (even without
task-specific label information) can be used to boost CI progression trajectory
prediction. However, previous studies seldom take advantage of such brain
anatomy prior. To this end, this paper proposes a brain anatomy prior modeling
(BAPM) framework to forecast the clinical progression of cognitive impairment
with small-sized target MRIs by exploring anatomical brain structures.
Specifically, the BAPM consists of a pretext model and a downstream model, with
a shared brain anatomy-guided encoder to model brain anatomy prior explicitly.
Besides the encoder, the pretext model also contains two decoders for two
auxiliary tasks (i.e., MRI reconstruction and brain tissue segmentation), while
the downstream model relies on a predictor for classification. The brain
anatomy-guided encoder is pre-trained with the pretext model on 9,344 auxiliary
MRIs without diagnostic labels for anatomy prior modeling. With this encoder
frozen, the downstream model is then fine-tuned on limited target MRIs for
prediction. We validate the BAPM on two CI-related studies with T1-weighted
MRIs from 448 subjects. Experimental results suggest the effectiveness of BAPM
in (1) four CI progression prediction tasks, (2) MR image reconstruction, and
(3) brain tissue segmentation, compared with several state-of-the-art methods.

本文提出了一种大脑解剖结构模型 (BAPM) 的框架，通过探索大脑解剖结构来预测认知能力恶化的临床进展，验证了 BAPM 在四项认知能力恶化进展预测任务以及磁共振成像重建和脑组织分割方面与几种最先进方法的比较表明其有效性。