Fine-Grained Permeable Surface Mapping through Parallel U-Net

Permeable surface mapping, which mainly is the identification of surface materials that will percolate, is essential for various environmental and civil engineering applications, such as urban planning, stormwater management, and groundwater modeling. Traditionally, this task involves labor-intensive manual classification, but deep learning offers an efficient alternative. Although several studies have tackled aerial image segmentation, the challenges in permeable surface mapping arid environments remain largely unexplored because of the difficulties in distinguishing pixel values of the input data and due to the unbalanced distribution of its classes. To address these issues, this research introduces a novel approach using a parallel U-Net model for the fine-grained semantic segmentation of permeable surfaces. The process involves binary classification to distinguish between entirely and partially permeable surfaces, followed by fine-grained classification into four distinct permeability levels. Results show that this novel method enhances accuracy, particularly when working with small, unbalanced datasets dominated by a single category. Furthermore, the proposed model is capable of generalizing across different geographical domains. Domain adaptation is explored to transfer knowledge from one location to another, addressing the challenges posed by varying environmental characteristics. Experiments demonstrate that the parallel U-Net model outperforms the baseline methods when applied across domains. To support this research and inspire future research, a novel permeable surface dataset is introduced, with pixel-wise fine-grained labeling for five distinct permeable surface classes. In summary, in this work, we offer a novel solution to permeable surface mapping, extend the boundaries of arid environment mapping, introduce a large-scale permeable surface dataset, and explore cross-area applications of the proposed model. The three contributions are enhancing the efficiency and accuracy of permeable surface mapping while progressing in this field.

Classification of clinically relevant intravascular volume status using point of care ultrasound and machine learning

Purpose

This is a foundational study in which multiorgan system point of care ultrasound (POCUS) and machine learning (ML) are used to mimic physician management decisions regarding the functional intravascular volume status (IVS) and need for diuretic therapy. We present this as an impactful use case of an application of ML in aided decision making for clinical practice. IVS represents complex physiologic interactions of the cardiac, renal, pulmonary, and other organ systems. In particular, we focus on vascular congestion and overload as an evolving concept in POCUS diagnosis and clinical relevance. It is critical for physicians to be able to evaluate IVS without disrupting workflow or exposing patients to unnecessary testing, radiation, or cost. This work utilized a small retrospective dataset as a feasibility test for ML binary classification of diuretic administration validated with clinical decision data. Future work will be directed toward artificial intelligence (AI) delivery at the bedside and assessment of the impact on patient-centered outcomes and physician workflow improvement.

Approach

We retrospectively reviewed and processed 1039 POCUS video clips, including cardiac, thoracic, and inferior vena cava (IVC) views. Multiorgan POCUS clips were correlated with clinical data extracted from the electronic health record and deidentified for algorithm training and validation. We implemented a two-stream three-dimensional (3D) deep learning approach that fuses heart and IVC data to perform binary classification of the need for diuretic use.

Results

Our proposed approach achieves high classification accuracy (84%) for the determination of diuretic use with 0.84 area under the receiver operating characteristic curve.

Conclusions

Our two-stream 3D deep neural network is able to classify POCUS video clips that match physicians' classification for or against diuretic use with high accuracy. This serves as a foundational step in the progress toward AI-aided diagnosis and AI implementation in the field of IVS evaluation by POCUS.

AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides

Antibiotic resistance is a critical public health problem. Each year ∼2.8 million resistant infections lead to more than 35 000 deaths in the U.S. alone. Antimicrobial peptides (AMPs) show promise in treating resistant infections. However, applications of known AMPs have encountered issues in development, production, and shelf-life. To drive the development of AMP-based treatments, it is necessary to create design approaches with higher precision and selectivity toward resistant targets. Previously, we developed AMPGAN and obtained proof-of-concept evidence for the generative approach to design AMPs with experimental validation. Building on the success of AMPGAN, we present AMPGAN v2, a bidirectional conditional generative adversarial network (BiCGAN)-based approach for rational AMP design. AMPGAN v2 uses generator-discriminator dynamics to learn data-driven priors and controls generation using conditioning variables. The bidirectional component, implemented using a learned encoder to map data samples into the latent space of the generator, aids iterative manipulation of candidate peptides. These elements allow AMPGAN v2 to generate candidates that are novel, diverse, and tailored for specific applications, making it an efficient AMP design tool.

Machine deep learning accurately detects endoleak after endovascular abdominal aortic aneurysm repair

Objective

The objective of this study was to develop a machine deep learning algorithm for endoleak detection and measurement of aneurysm diameter, area, and volume from computed tomography angiography (CTA).

Methods

Digital Imaging and Communications in Medicine files representing three-phase postoperative CTA images (N = 334) of 191 unique patients undergoing endovascular aneurysm repair for infrarenal abdominal aortic aneurysm (AAA) with a variety of commercial devices were used to train a deep learning pipeline across four tasks. The RetinaNet object-detection convolutional neural network (CNN) architecture was trained to predict bounding boxes around the axial CTA slices that were then stitched together in two dimensions into a smaller region containing the aneurysm. Multiclass endoleak detection and segmentation of the AAA, endograft, and endoleak were performed on this smaller region. Segmentations on a single randomly selected contrast from each scan included 33 full and 68 partial segmentations for endograft and AAA and 99 full segmentations for endoleak. A modified version of ResNet-50 CNN was used to detect endoleak on individual axial slices. A three-dimensional U-Net CNN model was trained on the task of dense three-dimensional segmentation and used to measure diameter and volume with a specially designed loss function. We made use of fivefold cross-validation to evaluate model performance for each step, splitting training and testing data at each fold, such that multiple scans from the same patient were preserved with the same fold. Algorithm predictions for endoleak were compared with the radiology report and with a subset of CTA images independently read by two vascular specialists.

Results

The localization portion of the network accurately predicted a region of interest containing the AAA in 99% of cases. The best model of binary endoleak detection obtained an area under the receiver operating characteristic curve of 0.94 ± 0.03 with an optimized accuracy of 0.89 ± 0.03 on a balanced data set. An introduced postprocessing algorithm for determining maximum diameter was used on both the predicted AAA segmentation and ground truth segmentation, predicting on average an absolute diameter error of 2.3 ± 2.0 mm by 1.4 ± 1.7 mm for each measurement, respectively. The algorithm measured AAA and endograft volume accurately (Dice coefficient, 0.95 ± 0.2) with an absolute volume error of 10.1 ± 9.1 mL. The algorithm measured endoleak volume less accurately, with a Dice score of 0.53 ± 0.21 and an average absolute volume error of 1.2 ± 1.9 mL.

Conclusions

This machine learning algorithm shows promise in augmenting a human's ability to interpret postoperative CTA images and may help improve surveillance after endovascular aneurysm repair. External validation on larger data sets and prospective study are required before the algorithm can be clinically applicable.

This manuscript describing the application of machine learning for endoleak has clinical relevance to endovascular abdominal aortic aneurysm follow-up. The techniques described herein may be more broadly applied to the diagnosis of aortic disease. In the future, vascular surgeons will benefit from integrating such artificial intelligence algorithms into their practice.

Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach

BACKGROUND

A majority of adults in the United States are exposed to a potentially traumatic event but only a handful go on to develop impairing mental health conditions such as posttraumatic stress disorder (PTSD).

OBJECTIVE

Identifying those at elevated risk shortly after trauma exposure is a clinical challenge. The aim of this study was to develop computational methods to more effectively identify at-risk patients and, thereby, support better early interventions.

METHODS

We proposed machine learning (ML) induction of models to automatically predict elevated PTSD symptoms in patients 1 month after a trauma, using self-reported symptoms from data collected via smartphones.

RESULTS

We show that an ensemble model accurately predicts elevated PTSD symptoms, with an area under the curve (AUC) of .85, using a bag of support vector machines, naive Bayes, logistic regression, and random forest algorithms. Furthermore, we show that only 7 self-reported items (features) are needed to obtain this AUC. Most importantly, we show that accurate predictions can be made 10 to 20 days posttrauma.

CONCLUSIONS

These results suggest that simple smartphone-based patient surveys, coupled with automated analysis using ML-trained models, can identify those at risk for developing elevated PTSD symptoms and thus target them for early intervention.