RESUMEN
Hinman syndrome, as is the case with many other rare conditions, is a disease very commonly under-considered or missed in the diagnosis of patients with the presenting symptoms. Clinical and radiographic manifestations of the condition are easily confused with neurogenic bladder without proper history collection and neurological examination. Patients typically present with symptoms including enuresis, urinary retention, reoccurring urinary tract infections, and encopresis. Imaging often shows hydroureteronephrosis and marked bladder wall thickening. While these signs are characteristic of neurogenic bladder, routine neurologic examinations and MRIs of patients with Hinman syndrome often show normal results, and their condition is currently thought to be an acquired behavioral and psychological disorder, often associated with abnormal family dynamics. We present the case of a 14-year-old boy, who presented to the emergency department with nausea, bilateral flank pain, and urinary retention. The patient had an over seven-year history of recurrent urinary tract infections (UTI) and intermittent encopresis and followed up with different providers. Due to the patient's extensive history and the failure of previous treatments, he was evaluated for causes of neurogenic bladder, but the MRI of the lumbar spine was normal. Fluoroscopic voiding cystourethrogram (VCUG) was ordered and demonstrated abnormal and trabeculated contour of the urinary bladder with bilateral vesicoureteral reflux consistent with the diagnosis of Hinman syndrome.
RESUMEN
Artificial intelligence (AI) is a path-breaking advancement for many industries, including the health care sector. The expeditious development of information technology and data processing has led to the formation of recent tools known as artificial intelligence. Radiology has been a portal for medical technological advancements, and AI will likely be no dissimilar. Radiology is the platform for many technological advances in the medical field; AI can undoubtedly impact every step of a radiologist's workflow. AI can simplify every activity like ordering and scheduling, protocoling and acquisition, image interpretation, reporting, communication, and billing. AI has eminent potential to augment efficiency and accuracy throughout radiology, but it also possesses inherent drawbacks and biases. We collected studies that were published in the past five years using PubMed as our database. We chose studies that were relevant to artificial intelligence in radiology. We mainly focused on the overview of AI in radiology, components included in the functioning of AI, AI assisting in the radiologists' workflow, ethical aspects of AI, challenges, and biases that AI experiencing together with some clinical applications of AI. Of all 33 studies, we found 15 articles discussed the overview and components of AI, five articles about AI affecting radiologist's workflow, five articles related to challenges and biases in AI, two articles discussed ethical aspects of AI, and six articles about practical implications of AI. We found out that the application of AI could make time-dependent tasks that can be performed effortlessly, permitting radiologists more time and opportunities to engage in patient care via increased time for consultation and development in imaging and extracting useful data from those images. AI could only be an aid to radiologists but will not replace a radiologist. Radiologists who use AI to their benefit, rather than to avoid it out of fear, might supersede those radiologists who do not. Substantial research should be done regarding the practical implications of AI algorithms for residents curriculum and the benefits of AI in radiology.