AIMC Topic: Humans

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Medial versus lateral camera port placement for robotic nephron-sparing surgery: evaluating surgical complications and outcomes.

Journal of robotic surgery
As the growing popularity of robotic-assisted laparoscopic procedures for the treatment of renal cancer increases, there exists a variation in surgical technique among institutions and surgeons alike. One variation that exists in robotics is the anat...

Generative adversarial network-based deep learning approach in classification of retinal conditions with optical coherence tomography images.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To determine whether a deep learning approach using generative adversarial networks (GANs) is beneficial for the classification of retinal conditions with Optical coherence tomography (OCT) images.

Improving liver lesions classification on CT/MRI images based on Hounsfield Units attenuation and deep learning.

Gene expression patterns : GEP
The early sign detection of liver lesions plays an extremely important role in preventing, diagnosing, and treating liver diseases. In fact, radiologists mainly consider Hounsfield Units to locate liver lesions. However, most studies focus on the ana...

A Retrospective Analysis of Pedicle Screw Placement Accuracy Using the ExcelsiusGPS Robotic Guidance System: Case Series.

Operative neurosurgery (Hagerstown, Md.)
BACKGROUND: Robotic guidance has become widespread in spine surgery. Although the intent is improved screw placement, further system-specific data are required to substantiate this intention for pedicle screws in spinal stabilization constructs.

X-ray dose profiles using artificial neural networks.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
This paper introduces a novel computational method to simulate and predict radiation dose profiles in a water phantom irradiated by X-rays of 6 and 15 MV at different depths and field sizes using Artificial Neural Networks within the error margin req...

Human induced pluripotent stem cell formation and morphology prediction during reprogramming with time-lapse bright-field microscopy images using deep learning methods.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Human induced pluripotent stem cells (hiPSCs) represent an ideal source for patient specific cell-based regenerative medicine; however, efficiency of hiPSC formation from reprogramming cells is low. We use several deep-learn...

TwinEDA: a sustainable deep-learning approach for limb-position estimation in preterm infants' depth images.

Medical & biological engineering & computing
Early diagnosis of neurodevelopmental impairments in preterm infants is currently based on the visual analysis of newborns' motion patterns by trained operators. To help automatize this time-consuming and qualitative procedure, we propose a sustainab...

Towards digital diagnosis of malaria: How far have we reached?

Journal of microbiological methods
The need for precise and early diagnosis of malaria and its distinction from other febrile illnesses is no doubt a prerequisite, primarily when standard rapid diagnostic tests (RDTs) cannot be totally relied upon. At the time of disease outbreaks, th...

Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: Methodology review and benchmark.

Journal of biomedical informatics
Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medicine is about the causal effects. The average treatment effect has long been studied as a measure of ...