Artificial Intelligence in Dermatopathology: New Insights and Perspectives

In recent years, an increasing enthusiasm has been observed towards artificial intelligence and machine learning, involving different areas of medicine. Among these, although still in the embryonic stage, the dermatopathological field has also been partially involved, with the attempt to develop and train algorithms that could assist the pathologist in the differential diagnosis of complex melanocytic lesions. In this article, we face this new challenge of the modern era, carry out a review of the literature regarding the state of the art and try to determine promising future perspectives

Pediatric Headache in Primary Care and Emergency Departments: Consensus with RAND/UCLA Method

Headache is the most frequent neurological symptom in childhood and the main reason for admission to pediatric emergency departments. The aim of this consensus document is to define a shared clinical pathway between primary care pediatricians (PCP) and hospitals for the management of children presenting with headache. For the purposes of the study, a group of hospital pediatricians and a group of PCP from the Emilia Romagna's health districts were selected to achieve consensus using the RAND/UCLA appropriateness method. Thirty-nine clinical scenarios were developed: for each scenario, participants were asked to rank the appropriateness of each option from 1 to 9. Agreement was reached if >= 75% of participants ranked within the same range of appropriateness. The answers, results, and discussion helped to define the appropriateness of procedures with a low level of evidence regarding different steps of the diagnostic-therapeutic process: primary care evaluation, emergency department evaluation, hospital admission, acute therapy, prophylaxis, and follow-up. The RAND proved to be a valid method to value appropriateness of procedures and define a diagnostic-therapeutic pathway suitable to the local reality in the management of pediatric headache. From our results, some useful recommendations were developed for optimizing the healthcare professionals' network among primary care services and hospitals

Corpus callosum abnormalities: neuroimaging, cytogenetics and clinical characterization of a very large multicenter Italian series

Corpus callosum abnormalities (CCA) have an estimated prevalence ranging from 0.3% up to 0.7% in patients undergoing brain imaging. CCA can be identified incidentally, or can be part of a developmental disease. We performed a retrospective study of 551 patients, identified non-syndromic (NS) CCA and syndromic (S) CCA, reviewing clinical features, neuroradiological aspects, genetic etiology, and chromosomal microarray (CMA) results. Syndromic CCA subjects were prevalent (60%) and they showed the most severe clinical features. Cortical malformations and cerebellar anomalies were 23% of cerebral malformation associated to CCA (plus), 23 and 14% respectively in syndromic forms. A clinical and/or genetic diagnosis was obtained in 37% of syndromic CCA including chromosomal rearrangements on high-resolution karyotype (18%), microdeletion/microduplication syndromes (31%) and monogenic diseases (51%). Non-syndromic CCA anomalies had mildest clinical features, although intellectual disability was present in 49% of cases and epilepsy in 13%. CMA diagnostic rate in our cohort of patients ranged from 11 to 23% (NS to S). A high percentage of patients (76% 422/551) remain without a diagnosis. Combined high resolution CMA studies and next-generation sequencing (NGS) strategies will increase the probability to identify new causative genes of CCA and to redefine genotype–phenotype correlation