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Above and Beyond Age: Prediction of Major Postoperative Adverse Events in Head and Neck Surgery.

Published in The Annals of otology, rhinology, and laryngology

Major postoperative adverse events (MPAEs) following head and neck surgery are not infrequent and lead to significant morbidity. The objective of this study was to ascertain which factors are most predictive of MPAEs in patients undergoing head and neck surgery. A cohort study was carried out based on data from patients registered in the National S...

Bias in, bias out: Underreporting and underrepresentation of diverse skin types in machine learning research for skin ca...

Published in Journal of the American Academy of Dermatology

Developing artificial intelligence technology for pediatric pulmonology: Lessons from COVID-19.

Published in Pediatric pulmonology

Data-driven myelin water imaging based on T1 and T2 relaxometry.

Published in NMR in biomedicine

Long acquisition times preclude the application of multiecho spin echo (MESE) sequences for myelin water fraction (MWF) mapping in daily clinical practice. In search of alternative methods, previous studies of interest explored the biophysical modeling of MWF from measurements of different tissue properties that can be obtained in scan times shorte...

Early prediction of clinical deterioration using data-driven machine-learning modeling of electronic health records.

Published in The Journal of thoracic and cardiovascular surgery

To develop and evaluate a high-dimensional, data-driven model to identify patients at high risk of clinical deterioration from routinely collected electronic health record (EHR) data. In this single-center, retrospective cohort study, 488 patients with single-ventricle and shunt-dependent congenital heart disease

Prediction of ameloblastoma recurrence using random forest-a machine learning algorithm.

Published in International journal of oral and maxillofacial surgery

The purpose of this study was to investigate whether ameloblastoma with a high likelihood of recurrence can be predicted using random forest model, a machine learning algorithm. Data were collected from patients treated for ameloblastoma between 1999 and 2019 at the University of Hong Kong. Fourteen clinical parameters were used to grow the decisio...

Machine Learning Algorithms for Classification of First-Trimester Fetal Brain Ultrasound Images.

Published in Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine

To evaluate the feasibility of machine learning (ML) tools for segmenting and classifying first-trimester fetal brain ultrasound images. Two image segmentation methods processed high-resolution fetal brain images obtained during the nuchal translucency scan: "Statistical Region Merging" (SRM) and "Trainable Weka Segmentation" (TWS), with training a...

Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international clas...

Published in Topics in stroke rehabilitation

To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairments and level of activity and participation in persons with stroke during rehabilitation. This scoping review maps ...

Deep learning-based artificial intelligence model for identifying swallow types in esophageal high-resolution manometry....

Published in Neurogastroenterology and motility : the official journal of the European Gastrointestinal Motility Society

This study aimed to build and evaluate a deep learning, artificial intelligence (AI) model to automatically classify swallow types based on raw data from esophageal high-resolution manometry (HRM). HRM studies on patients with no history of esophageal surgery were collected including 1,741 studies with 26,115 swallows labeled by swallow type (norma...

Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept.

Published in The American journal of bioethics : AJOB

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress' prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical i...