Seizure prediction may be the solution for epileptic patients whose drugs and surgery do not control seizures. Despite 46 years of research, few devices/systems underwent clinical trials and/or are commercialized, where the most recent state‐of‐the‐art approaches, as neural networks models, are not used to their full potential. The latter demonstrates the existence of social barriers to new methodologies due to data bias, patient safety, and legislation compliance. In the form of literature review, we performed a qualitative study to analyze the seizure prediction ecosystem to find these social barriers. With the Grounded Theory, we draw hypotheses from data, while with the Actor‐Network Theory we considered that technology shapes social configurations and interests, being fundamental in healthcare. We obtained a social network that describes the ecosystem and propose research guidelines aiming at clinical acceptance. Our most relevant conclusion is the need for model explainability, but not necessarily intrinsically interpretable models, for the case of seizure prediction. Accordingly, we argue that it is possible to develop robust prediction models, including black‐box systems to some extent, while avoiding data bias, ensuring patient safety, and still complying with legislation, if they can deliver human‐ comprehensible explanations. Due to skepticism and patient safety reasons, many authors advocate the use of transparent models which may limit their performance and potential. Our study highlights a possible path, by using model explainability, on how to overcome these barriers while allowing the use of more computationally robust models.
Nasopharyngeal and otitis media tuberculosis are rare extrapulmonary manifestations of Mycobacterium tuberculosis infection. We present a case of a middle-aged woman with manifestations of both conditions along with a description of the anatomical and temporal evolution of the disease. This case also highlights the difficulty of diagnosis and management of this condition, requiring a multidisciplinary approach. Extrapulmonary tuberculosis must be considered in the differential diagnosis of multiple head and neck conditions, including refractory chronic rhinosinusitis and otitis.
A rinolitíase é uma patologia rara e pouco descrita na literatura. Um rinólito é uma massa calcificada das fossas nasais. Pode ser um achado acidental num doente assintomático ou ser causa de rinossinusite crónica e de destruição das fossas nasais e dos seios perinasais. É um diagnóstico a ter em conta nos casos de obstrução nasal e rinorreia unilaterais, sendo a endoscopia nasal e a tomografia computorizada fundamentais para o diagnóstico. O tratamento implica a sua remoção cirúrgica, podendo ser realizado sob anestesia local ou geral, geralmente por via endoscópica. Os autores apresentam dois casos clínicos que ilustram ambos os espectros desta condição e fazem uma revisão bibliográfica sobre o tema.
Despite 46 years of seizure prediction research, few devices/systems underwent clinical trials and/or are commercialised, where the most recent state-of-the-art approaches are not used to their full potential. This demonstrates the existence of social barriers to new methodologies. Based on the literature, we performed a qualitative study to analyse the seizure prediction ecosystem to find these barriers. With Grounded Theory and Actor-Network Theory, we draw hypothesis from data and considered that technology shapes social configurations and interests. For seizure prediction, as long as an algorithm proves to be useful to the patient, we conclude that we may only need to explain the model’s decisions, and not to necessarily obtain intrinsically interpretable models. Accordingly, we argue that it is possible to develop robust prediction models, including black-box systems to some extent, while avoiding data bias, ensuring patient safety, and still complying with legislation, as long as they can deliver human-comprehensible explanations.
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