Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection

Sensor, Signal, and Imaging Informatics in 2017.

Objective To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.Methods PubMed® and Web of Science® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.Results The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.ConclusionThe growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics

Exploring the use of AI in odontology for paediatric patients : a systematic integrative review

Introdução: A inteligência artificial (IA) é a capacidade que um computador tem de reproduzir um determinado raciocínio, planeamento e mesmo a criatividade semelhante à do ser humano. A relevância desta revisão reside na oportunidade de explorar a importância da IA na nossa vida moderna, no futuro fluxo de trabalho dos consultórios dentários, sendo a literatura escassa no âmbito da IA em Odontopediatria. Objetivo: Determinar de que forma a IA pode ser aplicada em odontologia pediátrica. Materiais e métodos: Foi realizada uma pesquisa bibliográfica na base de dados PubMed. Os resultados incluem estudos publicados que cumprem os critérios no período de 2013 até 23 de janeiro 2023. Resultados: Várias pesquisas foram realizadas em pacientes pediátricos em relação à estimativa de idade dentária, posicionamento dentário e diagnóstico de cárie. A maioria desses estudos encontrou conclusões positivas relativamente à precisão dos modelos de aprendizagem profunda aplicados à análise de imagens. Discussão: Na literatura enfatiza a importância de investigações adicionais com amostras mais significativas. A aplicação desses modelos no fluxo de trabalho odontológico e as preocupações éticas foram também discutidas. Conclusão: A AI mostra resultados promissores no campo da odontopediatria, mas mais pesquisas são necessárias, a regulamentação ética sobre privacidade de dados precisa ser adotada e aplicada.Introduction: Artificial intelligence (AI) is the ability of a computer to reproduce a certain reasoning, planning and even creativity similar to that of a human being. The relevance of this review lies in the opportunity to explore the importance of AI in our modern life, in the future workflow of dental offices, since literature is scarce in the field of AI in Paediatric Dentistry. Aim: To determine whether AI can be applied in paediatric dentistry. Materials and methods: A literature search was conducted in the PubMed database. The results include published studies meeting the criteria in the period from 2013 to January 23, 2023. Results: Several researches have been conducted in paediatric patients regarding dental age estimation, tooth positioning and caries diagnosis. Most of these studies found positive conclusions regarding the accuracy of deep learning models applied to image analysis. Discussion: In the literature the importance of further investigations with more significant samples is emphasised. The application of these models in the dental workflow and ethical concerns were also discussed. Conclusion: AI shows promising results in the field of paediatric dentistry, but more research is needed, ethical regulations on data privacy need to be adopted and enforced

Segmentation of fetal 2D images with deep learning: a review

Image segmentation plays a vital role in providing sustainable medical care in this evolving biomedical image processing technology. Nowadays, it is considered one of the most important research directions in the computer vision field. Since the last decade, deep learning-based medical image processing has become a research hotspot due to its exceptional performance. In this paper, we present a review of different deep learning techniques used to segment fetal 2D images. First, we explain the basic ideas of each approach and then thoroughly investigate the methods used for the segmentation of fetal images. Secondly, the results and accuracy of different approaches are also discussed. The dataset details used for assessing the performance of the respective method are also documented. Based on the review studies, the challenges and future work are also pointed out at the end. As a result, it is shown that deep learning techniques are very effective in the segmentation of fetal 2D images.info:eu-repo/semantics/publishedVersio

Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI

Brain network analysis is a useful approach to studying human brain disorders because it can distinguish patients from healthy people by detecting abnormal connections. Due to the complementary information from multiple modal neuroimages, multimodal fusion technology has a lot of potential for improving prediction performance. However, effective fusion of multimodal medical images to achieve complementarity is still a challenging problem. In this paper, a novel hierarchical structural-functional connectivity fusing (HSCF) model is proposed to construct brain structural-functional connectivity matrices and predict abnormal brain connections based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). Specifically, the prior knowledge is incorporated into the separators for disentangling each modality of information by the graph convolutional networks (GCN). And a disentangled cosine distance loss is devised to ensure the disentanglement's effectiveness. Moreover, the hierarchical representation fusion module is designed to effectively maximize the combination of relevant and effective features between modalities, which makes the generated structural-functional connectivity more robust and discriminative in the cognitive disease analysis. Results from a wide range of tests performed on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that the proposed model performs better than competing approaches in terms of classification evaluation. In general, the proposed HSCF model is a promising model for generating brain structural-functional connectivities and identifying abnormal brain connections as cognitive disease progresses.Comment: 4 figure