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Automated interpretation of ultrasound imaging of the heart (echocardiograms) could improve the detection and treatment of aortic stenosis (AS), a deadly heart disease. However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations. First, most methods rely on limited 2D cineloops, thereby ignoring widely available Spectral Doppler imaging that contains important complementary information about pressure gradients and blood flow abnormalities associated with AS. Second, obtaining labeled data is difficult. There are often far more unlabeled echocardiogram recordings available, but these remain underutilized by existing methods. To overcome these limitations, we introduce Semi-supervised Multimodal Multiple-Instance Learning (SMMIL), a new deep learning framework for automatic interpretation for structural heart diseases like AS. During training, SMMIL can combine a smaller labeled set and an abundant unlabeled set of both 2D and Doppler modalities to improve its classifier. When deployed, SMMIL can combine information from all available images to produce an accurate study-level diagnosis of this life-threatening condition. Experiments demonstrate that SMMIL outperforms recent alternatives, including two medical foundation models. 

Automated interpretation of ultrasound imaging of the heart (echocardiograms) could improve the detection and treatment of aortic stenosis (AS), a deadly heart disease. However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations. First, most methods rely on limited 2D cineloops, thereby ignoring widely available Spectral Doppler imaging that contains important complementary information about pressure gradients and blood flow abnormalities associated with AS. Second, obtaining labeled data is difficult. There are often far more unlabeled echocardiogram recordings available, but these remain underutilized by existing methods. To overcome these limitations, we introduce Semi-supervised Multimodal Multiple-Instance Learning (SMMIL), a new deep learning framework for automatic interpretation for structural heart diseases like AS. During training, SMMIL can combine a smaller labeled set and an abundant unlabeled set of both 2D and Doppler modalities to improve its classifier. When deployed, SMMIL can combine information from all available images to produce an accurate study-level diagnosis of this life-threatening condition. Experiments demonstrate that SMMIL outperforms recent alternatives, including two medical foundation models. 

To inform public health interventions, researchers have developed models to forecast opioid-related overdose mortality. These efforts often have limited overlap in the models and datasets employed, presenting challenges to assessing progress in this field. Furthermore, common error-based performance metrics, such as root mean squared error (RMSE), cannot directly assess a key modeling purpose: the identification of priority areas for interventions. We recommend a new intervention-aware performance metric, Percentage of Best Possible Reach (%BPR). We compare metrics for many published models across two distinct geographic settings, Cook County, Illinois and Massachusetts, assuming the budget to intervene in 100 census tracts out of 1000s in each setting. The top-performing models based on RMSE recommend areas that do not always reach the most possible overdose events. In Massachusetts, the top models preferred by %BPR could have reached 18 additional fatal overdoses per year in 2020-2021 compared to models favored by RMSE. In Cook County, the different metrics select similar top-performing models, yet other models with similar RMSE can have significant variation in %BPR. We further find that simple models often perform as well as recently published ones. We release open code and data for others to build upon. 

Nonlinear aeroelastic limit-cycle oscillations (LCOs) have become an area of interest due to both detrimental effects on flying vehicles and use in renewable energy harvesting. Initial studies on the interaction between aeroelastic systems and incoming flow disturbances have shown that disturbances can have significant effects on LCO amplitude, with some cases resulting in spontaneous annihilation of the LCO. This paper explores this interaction through wind-tunnel experiments using a variable-frequency disturbance generator to produce flow disturbances at frequencies near the inherent LCO frequency of an aeroelastic system with pitching and heaving degrees of freedom. The results show that incoming disturbances produced at frequencies approaching the LCO frequency from below produce a cyclic growth-decay in LCO amplitude that resembles interference between multiple sine waves with slightly varying frequencies. An aeroelastic inverse technique is applied to the results to study the transfer of energy between the pitching and heaving degrees of freedom as well as the aerodynamic power moving into and out of the system. Finally, the growth-decay cycles are shown to both excite LCOs in an initially stationary wing and annihilate preexisting LCOs in the same wing by appropriately timing the initiation and termination of disturbance generator motion.