Bioterrorism

Response to the Comments on "Situating Artificial Intelligence in Surgery, a Focus on Disease Severity".

Author response to: Shortening surgical training through robotics: randomized clinical trial of laparoscopic versus robotic surgical learning curves.

An artificial neural network model based on DNA damage response genes to predict outcomes of lower-grade glioma patients.

Although the prognosis of lower-grade glioma (LGG) patients is better than others, outcomes are high...

Use of Medical Information and Digital Services for Self-Empowerment before, during, and after a Major Disaster.

Disaster response procedures have been developed and improved following the Great East Japan Earthqu...

A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma.

BACKGROUND: To assess the additive value of dual-energy CT (DECT) over single-energy CT (SECT) to ra...

Multiplex computational pathology for treatment response predication.

Recently published in Science, AstroPath outlines a standardized workflow for multiplex immunofluore...

Author's Response to "Re: Magnetic-Assisted Robotic and Laparoscopic Renal Surgery: Initial Clinical Experience with Levita Magnetic Surgical System" by Palese et al.

Privacy-protecting, reliable response data discovery using COVID-19 patient observations.

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic heal...

Towards realizing the vision of precision medicine: AI based prediction of clinical drug response.

Accurate and individualized prediction of response to therapies is central to precision medicine. Ho...

Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE-an enhanced deconvolution method.

The accurate quantification of tumor-infiltrating immune cells turns crucial to uncover their role i...

Response to Letter to the Editor "Spinal Navigation Is Not the Same as Robotic Assistance in Surgery".

Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images.

BACKGROUND: Currently, only a fraction of patients with non-small cell lung cancer (NSCLC) treated w...

Compressive recovery of smartphone RGB spectral sensitivity functions.

Spectral response (or sensitivity) functions of a three-color image sensor (or trichromatic camera) ...

Letter to the editor in response to "Risk prediction of delirium in hospitalized patients using machine learning: an implementation and prospective evaluation study".

Deep learning for drug response prediction in cancer.

Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount ...

Interaction modeling and classification scheme for augmenting the response accuracy of human-robot interaction systems.

BACKGROUND: Human-robot interaction (HRI) is becoming a current research field for providing granula...

Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features.

PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast cancer (LABC) and h...

Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections.

BACKGROUND: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely qua...

Big Data Analytics + Virtual Clinical Semantic Network (vCSN): An Approach to Addressing the Increasing Clinical Nuances and Organ Involvement of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has revealed deep gaps in our understanding of the ...

[Application of artificial intelligence in prevention and control of COVID-19 in Guangzhou city].

Since the outbreak of COVID-19, it has spread rapidly throughout the country and even in the world. ...

Machine intelligence for early targeted precision management and response to outbreaks of respiratory infections.

OBJECTIVES: To evaluate the utility of machine learning (ML) for the management of Medicare benefici...