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Currently there is no early dehydration detection system using temperature and pH as indicators. A sensor could alert the wearer and others of low hydration levels, which would normally be difficult to catch prior to more serious complications resulting from dehydration. In this study, a protein fluorophore, green fluorescent protein (GFP), and a chemical fluorophore, fluorescein, were tested for a change in fluorescence in response to increased temperature or decreased pH. Reversing the pH change did not restore GFP fluorescence, but that of fluorescein was re-established. This finding suggests that fluorescein could be used as a reusable sensor for a dehydration-related pH change.

The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.

The authors use machine learning to develop an evidence-based detection tool for identifying human trafficking.

Intelligent vehicles utilize a combination of video-enabled object detection and radar data to traverse safely through surrounding environments. However, since the most momentary missteps in these systems can cause devastating collisions, the margin of error in the software for these systems is small. In this paper, we hypothesized that a novel object detection system that improves detection accuracy and speed of detection during adverse weather conditions would outperform industry alternatives in an average comparison.

The authors compare neuroimaging datasets to identify potential new biomarkers for earlier detection of Alzheimer's disease.

The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.

The authors looked factors that could lead to earlier diagnosis of cardiovascular disease thereby improving patient outcomes. They found that advances in imaging and electrocardiography contribute to earlier detection of cardiovascular disease.

Lung cancer is highly fatal, largely due to late diagnoses, but early detection can greatly improve survival. This study developed three models to enhance early diagnosis: an MLP for clinical data, a CNN for imaging data, and a hybrid model combining both.

Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.

The authors trained a machine learning model to detect kidney stones based on characteristics of urine. This method would allow for detection of kidney stones prior to the onset of noticeable symptoms by the patient.