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Splenopancreatic fusion is an uncommon finding, usually only seen as part of the splenopancreatic field abnormality associated with trisomy 13. It may present itself either as ectopic splenic tissue in the cauda pancreatis, as ectopic pancreatic tissue in the spleen or accessory spleen, or as fusion of the cauda pancreatis and splenic hilum. In this study, we report four unrelated congenital anomaly cases presenting trisomy 21, osteocraniostenosis syndrome, isolated congenital heart defect, and oligohydramnios sequence due to prune belly syndrome, in which fusion was observed. This demonstrates that, although it may be more common in trisomy 13, this phenomenon should not be interpreted as pathognomonic to that syndrome.

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BACKGROUND Cancer occupies the second leading cause of death in the world, behind only cardiovascular diseases. Among cancers, skin cancer is the most frequent in Brazil and worldwide. The University Extension Program entitled Dermatological Assistance Program to Pomeranian Farmers in Espírito Santo (PAD) of the Federal University of Espírito Santo, has been promoting prevention, diagnosis, and adequate treatment in the Pomeranian population of Espírito Santo since 1986, through joint efforts of medical care. The result of these joint efforts represents, on average, 300 doctor’s appointments, 500 to 900 cryotherapies, and 100 surgeries per county where the visits occur, promoted once a month. OBJECTIVE Currently, there is no set of public cancer-related data in the literature that provides information about clinical images and medical histories of affected patients. In partnership with the Engineering and Computer Science Sectors, the Dermatological Analysis Software (SADE) was created to store data and images of the skin lesions of patients operated by the program. The goal of this study was to evaluate the scenario of skin cancer in the communities served by the PAD, based on data stored at SADE between 2018 and 2019, totaling 2,935 visits during this period. METHODS This is a retrospective study carried out from the database collected via the SADE platform (Dermatological Analysis Software). The data is collected using the smartphone application, which connects to the local internet server to store the data. The application was developed using a specific type of Deep Learning model known as Convolutional Neural Networks (CNN). This model is trained using clinical images and patient demographic data collected using the software described above. RESULTS In view of the neoplastic lesions, 1,201 lesions were removed, which after histopathological examination showed 593 basal cell carcinomas (BCC), 95 squamous cell carcinomas (SCC), 81 melanomas and 48 associated BCC and SCC. CONCLUSIONS The results highlight the potential of the software to compose an application in the future that will help doctors in remote locations who have no training in dermatology, optimizing the referral of patients with suspected skin lesions to the specialist, aiming at a faster treatment and suggesting differential diagnoses that may not have been considered. The application of technological instruments in the identification of cancer is an increasingly current reality and its use is already widely used.