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In the battle against Alzheimer's disease, early detection is critical to mitigating symptoms in patients. Here, the authors use a collection of MRI scans, layering with deep learning computer modeling, to investigate early stages of AD which can be hard to catch by human eye. Their model is successful, able to outperform previous models, and detected regions of interest in the brain for further consideration.

AI analysis of brain scans offers promise for helping doctors diagnose brain tumors. Haider and Drosis explore this field by developing machine learning models that classify brain scans as "cancer" or "non-cancer" diagnoses.

Although the 5-year survival rate for colorectal cancer is below 10%, it increases to greater than 90% if it is diagnosed early. We hypothesized from our research that analyzing non-synonymous single nucleotide variants (SNVs) in a patient's exome sequence would be an indicator for high genetic risk of developing colorectal cancer.

Effective treatment of depression requires early detection. Depressive symptoms overlap with olfactory regions, which led to several studies of the correlation between sense of smell and depression. The alarming rise of depression, its related crimes, suicides, and lack of inexpensive, quick tools in detecting early depression — this study aims in demonstrating decreased olfaction and depression correlation. Forty-two subjects (ages 13-83) underwent POC-MON (Pocket Lemon) assessment — an oven-dried lemon peel sniff test, subjected to distance measurement when odor first detected (threshold) and completed Patient Health Questionnaires (PHQ-9). POC-MON and PHQ-9 scores yielded a correlation of 20% and 18% for the right and left nostrils, respectively. Among male (n=17) subjects, the average distance of POC-MON and PHQ-9 scores produced a correlation of 14% and 16% for the right and left nostrils, respectively. Females (n=25) demonstrated a correlation of 28% and 21% for the right and left nostrils, respectively. These results suggest the correlation between olfaction and depression in diagnosing its early-stage, using a quick, inexpensive, and patient-friendly tool — POC-MON.

Here the authors investigated the use of an affordable gas sensor to detect volatile organic compound (VOC) emissions as an early indicator of tree disease, finding statistically significant differences in VOCs between diseased and non-diseased ash, beech, and maple trees. They suggest this sensor has potential for widespread early disease detection, but call for further research with larger sample sizes and diverse locations.

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.

Spinal degeneration has been linked to critical conditions such as osteoarthritis in adults aged 40+; while this condition is considered to be irreversible, we took interest in magnetic resonance imaging (MRI) for early detection of the condition. Ultimately, our purpose was to determine the effectiveness of a relatively novel T1rho method in the early detection of spinal degeneration, and we hypothesized that the early to mild progression of spinal degeneration would affect T1rho values following an MRI scan.

Nonalcoholic fatty liver disease (NAFLD) is one of the most prevalent chronic liver diseases worldwide, but there are few studied warning signs for early detection of the disease. Here, researchers study alterations that occur in a mouse model of NAFLD, which indicate the onset of NAFLD sooner. Earlier detection of diseases can lead to better prevention and treatment.

Droughts kill over 45,000 people yearly and affect the livelihoods of 55 million others worldwide, with climate change likely to worsen these effects. However, unlike other natural disasters (hurricanes, etc.), there is no early detection system that can predict droughts far enough in advance to be useful. Bora, Caulkins, and Joycutty tackle this issue by creating a drought prediction model.

Using a convolution neural network, these authors show machine learning can clinically diagnose breast cancer with high accuracy.