Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and “motivate” the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.
Electromagnetic actuation of micro-/milli-sized agents has traditionally relied on large electromagnets positioned at considerable distances from the agents. As a result, the electromagnets consume kilowatts of power to overcome the limited generation of magnetic field gradients. Miniaturized electromagnets offer an alternative approach for reducing power consumption via localized actuation of micro-/milli-sized agents. Typically, the generation of magnetic field gradients in the vicinity of a miniaturized electromagnet is comparable with traditional electromagnetic actuation systems. Miniaturized electromagnets can be positioned near target sites in microfluidic channels or ex vivo vasculatures. Thereby, localized trapping and actuation of magnetic micro-/milli-sized agents are carried out. This study introduces MagNeed -an electromagnetic actuation system composed of three needle-shaped electromagnets (NSEs). MagNeed can determine compact workspaces by positioning the NSEs at different spatial configurations. Each NSE generates magnetic field gradients (up to 3.5 T/m at 5 mm from the NSE tip axis) while keeping a maximum power consumption (0.5 W) and temperature (<42 • C). MagNeed is complemented by a framework that reconstructs the pose of the NSEs. Experiments test MagNeed and framework on a transparent Teflon tube (5 mm inner diameter). MagNeed demonstrates localized trapping and actuation of a 1 mm NdFeB bead against a flow of water and silica gel particles (1-3 mm diameter).
Deep Learning (DL) based classification algorithms have been shown to achieve top results in clinical diagnosis, namely with lung cancer datasets. However, the complexity and opaqueness of the models together with the still scant training datasets call for the development of explainable modeling methods enabling the interpretation of the results. To this end, in this paper we propose a novel interpretability approach and demonstrate how it can be used on a malignancy lung cancer DL classifier to assess its stability and congruence even when fed a low amount of image samples. Additionally, by disclosing the regions of the medical images most relevant to the resulting classification the approach provides important insights to the correspondent clinical meaning apprehended by the algorithm. Explanations of the results provided by ten different models against the same test sample are compared. These attest the stability of the approach and the algorithm focus on the same image regions.
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