AIMC Topic: Humans

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Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study.

PloS one
BACKGROUND: Osteoporosis has increased and developed into a serious public health concern worldwide. Despite the high prevalence, osteoporosis is silent before major fragility fracture and the osteoporosis screening rate is low. Abdomen-pelvic CT (AP...

Ethical issues in computational pathology.

Journal of medical ethics
This paper explores ethical issues raised by whole slide image-based computational pathology. After briefly giving examples drawn from some recent literature of advances in this field, we consider some ethical problems it might be thought to pose. Th...

Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives.

Nutrition (Burbank, Los Angeles County, Calif.)
Sarcopenia is a muscle disease which previously was associated only with aging, but in recent days it has been gaining more attention for its predictive value in a vast range of conditions and its potential link with overall health. Up to this point,...

Exploring the spatial reasoning ability of neural models in human IQ tests.

Neural networks : the official journal of the International Neural Network Society
Although neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has been measured in only few studies. In this work, we focus on spatial reasoning and explore the spat...

Development of an algorithm for intraoperative autofluorescence assessment of parathyroid glands in primary hyperparathyroidism using artificial intelligence.

Surgery
BACKGROUND: Previous work showed that normal and abnormal parathyroid glands exhibit different patterns of autofluorescence, with the former appearing brighter and more homogenous. However, an objective algorithm based on quantified measurements was ...

Deep learning with a convolutional neural network model to differentiate renal parenchymal tumors: a preliminary study.

Abdominal radiology (New York)
PURPOSE: With advancements in medical imaging, more renal tumors are detected early, but it remains a challenge for radiologists to accurately distinguish subtypes of renal parenchymal tumors. We aimed to establish a novel deep convolutional neural n...

Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Artificial intelligence advances have stimulated a new generation of autosegmentation, however clinical evaluations of these algorithms are lacking. This study assesses the clinical utility of deep learning-based autosegmentat...

Clinical advantages of robot-assisted partial nephrectomy versus laparoscopic partial nephrectomy in terms of global and split renal functions: A propensity score-matched comparative analysis.

International journal of urology : official journal of the Japanese Urological Association
OBJECTIVES: To identify predictors of renal function preservation, and to compare the global and split renal function outcomes of robot-assisted partial nephrectomy and laparoscopic partial nephrectomy.

HistoNet: A Deep Learning-Based Model of Normal Histology.

Toxicologic pathology
We introduce HistoNet, a deep neural network trained on normal tissue. On 1690 slides with rat tissue samples from 6 preclinical toxicology studies, tissue regions were outlined and annotated by pathologists into 46 different tissue classes. From the...