Somatic symptom disorder in dermatology

Somatic symptom disorder (SSD) is defined by the prominence of somatic symptoms associated with abnormal thoughts, feelings, and behaviors related to the symptoms, resulting in significant distress and impairment. Individuals with these disorders are more commonly encountered in primary care and other medical settings, including dermatology practice, than in psychiatric and other mental health settings. What defines the thoughts, feelings, and behaviors as abnormal is that they are excessive, that is, out of proportion to other patients with similar somatic symptoms, and that they result in significant distress and impairment. SSD may occur with or without the presence of a diagnosable dermatologic disorder. When a dermatologic disorder is present, SSD should be considered when the patient is worrying too much about his or her skin, spending too much time and energy on it, and especially if the patient complains of many nondermatologic symptoms in addition. The differential diagnosis includes other psychiatric disorders, including depression, anxiety disorders, delusions of parasitosis, and body dysmorphic disorder

Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.

Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are 1000× larger in size