AIMC Topic: Aged

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PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging.

Aging
Aging biomarkers are the qualitative and quantitative indicators of the aging processes of the human body. Estimation of biological age is important for assessing the physiological state of an organism. The advent of machine learning lead to the deve...

[Robot assisted sacroiliac screw placement with a modified method of screw path planning].

Zhonghua yi xue za zhi
To introduce a robot-assisted modified method of sacroiliac screw path planning in order to reduce the incidence of screw misplacement. The study involved 13 patients suffering from posterior pelvic injuries treated by percutaneous sacroiliac screw...

Clinical observation of the efficacy of low-molecular-weight heparin calcium in prophylaxis of the deep venous thrombosis following the gynecological tumor surgery.

Pakistan journal of pharmaceutical sciences
Present study is conducted to investigate the efficacy and safety of application of low-molecular-weight heparin calcium in the prophylaxis of deep venous thrombosis (DVT) following the laparoscopic surgery for gynecological tumors, so as to provide ...

Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders.

Neurosurgical focus
OBJECTIVEIf not anticipated and prearranged, hospital stay can be prolonged while the patient awaits placement in a rehabilitation unit or skilled nursing facility following elective spine surgery. Preoperative prediction of the likelihood of postope...

Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

Neurosurgical focus
OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD trea...

A machine learning approach to predict early outcomes after pituitary adenoma surgery.

Neurosurgical focus
OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when usin...

Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study.

Neurosurgical focus
OBJECTIVEGross-total resection (GTR) is often the primary surgical goal in transsphenoidal surgery for pituitary adenoma. Existing classifications are effective at predicting GTR but are often hampered by limited discriminatory ability in moderate ca...

Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model.

American journal of critical care : an official publication, American Association of Critical-Care Nurses
BACKGROUND: Hospital-acquired pressure injuries are a serious problem among critical care patients. Some can be prevented by using measures such as specialty beds, which are not feasible for every patient because of costs. However, decisions about wh...

Overachieving Municipalities in Public Health: A Machine-learning Approach.

Epidemiology (Cambridge, Mass.)
BACKGROUND: Identifying successful public health ideas and practices is a difficult challenge towing to the presence of complex baseline characteristics that can affect health outcomes. We propose the use of machine learning algorithms to predict lif...