AIMC Topic: Humans

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Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study.

European radiology
OBJECTIVES: To evaluate the performance of a deep learning radiomic nomogram (DLRN) model at predicting tumor relapse in patients with soft tissue sarcomas (STS) who underwent surgical resection.

An image-guided hybrid robot system for dental implant surgery.

International journal of computer assisted radiology and surgery
PURPOSE: Dental implant surgery is an effective method for remediating the loss of teeth. Robot is expected to increase the accuracy of dental implant surgery. However, most of them are industrial serial robot, with low stiffness and non-unique inver...

Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering.

Microscopy research and technique
Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effective...

Automated description of the mandible shape by deep learning.

International journal of computer assisted radiology and surgery
PURPOSE: The shape of the mandible has been analyzed in a variety of fields, whether to diagnose conditions like osteoporosis or osteomyelitis, in forensics, to estimate biological information such as age, gender, and race or in orthognathic surgery....

LwF-ECG: Learning-without-forgetting approach for electrocardiogram heartbeat classification based on memory with task selector.

Computers in biology and medicine
Most existing Electrocardiogram (ECG) classification methods assume that all arrhythmia classes are known during the training phase. In this paper, the problem of learning several successive tasks is addressed, where, in each new task, there are new ...

MedmeshCNN - Enabling meshcnn for medical surface models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: MeshCNN is a recently proposed Deep Learning framework that drew attention due to its direct operation on irregular, non-uniform 3D meshes. It outperformed state-of-the-art methods in classification and segmentation tasks of...

Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning.

Biochemical pharmacology
For medicines, the apparent membrane permeability coefficients (P) across human colorectal carcinoma cell line (Caco-2) monolayers under a pH gradient generally correlate with the fraction absorbed after oral intake. Furthermore, the in vitro P value...

Making space: the natural, cultural, cognitive and social niches of human activity.

Cognitive processing
This paper is in two parts. Part 1 examines the phenomenon of making space as a process involving one or other kind of legal decision-making, for example when a state authority authorizes the creation of a new highway along a certain route or of a ne...

Traffic Accident Data Generation Based on Improved Generative Adversarial Networks.

Sensors (Basel, Switzerland)
For urban traffic, traffic accidents are the most direct and serious risk to people's lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful effects. However, research scholars are often confronte...

Optimization of a Sports Activity Development Model Using Artificial Intelligence under New Curriculum Reform.

International journal of environmental research and public health
The recent curriculum reform in China puts forward higher requirements for the development of physical education. In order to further improve students' physical quality and motor skills, the traditional model was improved to address the lack of accur...