Inteligência Artificial em Radiologia: Do Processamento de Imagem ao Diagnóstico

The objective of this article is to present a view on the potential impact of Artificial Intelligence (AI) on processing medical images, in particular in relation to diagnostic. This topic is currently attracting major attention in both the medical and engineering communities, as demonstrated by the number of recent tutorials [1-3] and review articles [4-6] that address it, with large research hospitals, as well as engineering research centers contributing to the area. Furthermore, several large companies like General Electric (GE), IBM/Merge, Siemens, Philips or Agfa, as well as more specialized companies and startups are integrating AI into their medical imaging products. The evolution of GE in this respect is interesting. GE SmartSignal software was developed for industrial applications to identify impending equipment failures well before they happen. As written in the GE prospectus, with this added lead time, one can transform from reactive maintenance to a more proactive maintenance process, allowing the workforce to focus on fixing problems rather than looking for them. With this background experience from the industrial field, GE developed predictive analytics products for clinical imaging, that embodied the Predictive component of P4 medicine (predictive, personalized, preventive, participatory). Another interesting example is the Illumeo software from Philips that embeds adaptive intelligence, i. e. the capacity to improve its automatic reasoning process from its past experience, to automatically pop out related prior exams for radiology in face of a concrete situation. Actually, with its capacity to tackle massive amounts of data of different sorts (imaging data, patient exam reports, pathology reports, patient monitoring signals, data from implantable electrophysiology devices, and data from many other sources) AI is certainly able to yield a decisive contribution to all the components of P4 medicine. For instance, in the presence of a rare disease, AI methods have the capacity to review huge amounts of prior information when confronted to the patient clinical data

An ultrasonographic risk score for detecting symptomatic carotid atherosclerotic plaques

© 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.This paper proposes a risk score computed from ultrasound data that correlates to plaque activity. It has the twofold purpose of detecting symptomatic plaques and estimating the likelihood of the asymptomatic lesion to become symptomatic. The proposed ultrasonographic activity index (UAI) relies on the plaque active profile, which is a combination of the most discriminate ultrasound parameter associated with symptoms. These features are extracted by the automatic algorithm and also by the physician from the ultrasound images and from some transformations on it, such as monogenic decomposition, which is a novelty in this clinical problem. This information is used to compute a risk score from the conditional probabilities of either symptomatic or asymptomatic groups. Symptom detection performance is evaluated on a transversal dataset of 146 plaques, where UAI obtained 83.5% accuracy, 84.1% sensitivity, and 83.7% specificity. Performance is also assessed on a longitudinal study of 112 plaques, where UAI shows a significant improvement over the gold standard degree of stenosis, demonstrating higher power at predicting which asymptomatic plaques developed symptoms in an average follow-up of ten months. Results suggest that this score could have a positive impact on early stroke prevention and treatment planning.info:eu-repo/semantics/publishedVersio