Online violence against children has increased globally recently, demanding urgent attention. Competent authorities manually analyze abuse complaints to comprehend crime dynamics and identify patterns. However, the manual analysis of these complaints presents a challenge because it exposes analysts to harmful content during the review process. Given these challenges, we present a novel solution, an automated tool designed to analyze children's sexual abuse reports comprehensively. By automating the analysis process, our tool significantly reduces the risk of exposure to harmful content by categorizing the reports on three dimensions: Subject, Degree of Criminality, and Damage. Furthermore, leveraging our multidisciplinary team's expertise, we introduce a novel approach to annotate the collected data, enabling a more in-depth analysis of the reports. This approach improves the comprehension of fundamental patterns and trends, enabling law enforcement agencies and policymakers to create focused strategies in the fight against children's violence.

Early diagnosis of dementia, particularly in the prodromal stage (i.e., mild cognitive impairment, or MCI), has become a research and clinical priority but remains challenging. Automated analysis of the drawing process has been studied as a promising means for screening prodromal and clinical dementia, providing multifaceted information encompassing features, such as drawing speed, pen posture, writing pressure, and pauses. We examined the feasibility of using these features not only for detecting prodromal and clinical dementia but also for predicting the severity of cognitive impairments assessed using Mini-Mental State Examination (MMSE) as well as the severity of neuropathological changes assessed by medial temporal lobe (MTL) atrophy. We collected drawing data with a digitizing tablet and pen from 145 older adults of cognitively normal (CN), MCI, and dementia. The nested cross-validation results indicate that the combination of drawing features could be used to classify CN, MCI, and dementia with an AUC of 0.909 and 75.1% accuracy (CN vs. MCI: 82.4% accuracy; CN vs. dementia: 92.2% accuracy; MCI vs. dementia: 80.3% accuracy) and predict MMSE scores with an $R^2$ of 0.491 and severity of MTL atrophy with an $R^2$ of 0.293. Our findings suggest that automated analysis of the drawing process can provide information about cognitive impairments and neuropathological changes due to dementia, which can help identify prodromal and clinical dementia as a digital biomarker.

Researchers have developed a new method that uses artificial intelligence to analyze animal behavior. This opens the door to longer-term in-depth studies in the field of behavioral science--while also helping to improve animal welfare. The method is already being tested at Zurich Zoo. Researchers engaged in animal behavior studies often rely on hours upon hours of video footage which they manually analyze. Usually, this requires researchers to work their way through recordings spanning several weeks or months, laboriously noting down observations on the animals' behavior.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. UK Biobank (UKB) has recruited more than half a million volunteers from the United Kingdom (UK), collecting health-related information on genetics, lifestyle, blood biochemistry, and more. Ongoing medical imaging of 100,000 participants, with 70,000 follow-up sessions, will furthermore yield up to 170,000 MRIs, enabling image analysis of body composition, organs, and muscle. This work presents an experimental inference engine for automated analysis of 1.5T UKB neck-to-knee body MRI.

To develop and validate a deep learning–based method for automatic quantitative analysis of lower-extremity alignment. In this retrospective study, bilateral long-leg radiographs (LLRs) from 255 patients that were obtained between January and September of 2018 were included. For training data (n 109), a U-Net convolutional neural network was trained to segment the femur and tibia versus manual segmentation. For validation data (n 40), model parameters were optimized. Following identification of anatomic landmarks, anatomic and mechanical axes were identified and used to quantify alignment through the hip-knee-ankle angle (HKAA) and femoral anatomic-mechanical angle (AMA).

Individuals often express their opinions on social media platforms like Twitter and Facebook during public events such as the U.S. Presidential debate and the Oscar awards ceremony. Gleaning insights from these posts is of importance to analyzing the impact of the event. In this work, we consider the problem of identifying the segments and topics of an event that garnered praise or criticism, according to aggregated Twitter responses. We propose a flexible factorization framework, SocSent, to learn factors about segments, topics, and sentiments. To regulate the learning process, several constraints based on prior knowledge on sentiment lexicon, sentiment orientations (on a few tweets) as well as tweets alignments to the event are enforced. We implement our approach using simple update rules to get the optimal solution. We evaluate the proposed method both quantitatively and qualitatively on two large-scale tweet datasets associated with two events from different domains to show that it improves significantly over baseline models.