AIMC Topic: Pilot Projects
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Parallel, component training in robotic total mesorectal excision.
Journal of robotic surgery
Dec 14, 2022
There has been widespread adoption of robotic total mesorectal excision (TME) for rectal cancer in recent years. There is now increasing interest in training robotic novice surgeons in robotic TME surgery using the principles of component-based learn...
Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts.
The Lancet. Digital health
Dec 7, 2022
BACKGROUND: Endometrial cancer can be molecularly classified into POLE, mismatch repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) subgroups. We aimed to develop an interpretable deep learning pipeline for whole...
Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods.
Sensors (Basel, Switzerland)
Nov 23, 2022
Recent methods for automatic blood vessel segmentation from fundus images have been commonly implemented as convolutional neural networks. While these networks report high values for objective metrics, the clinical viability of recovered segmentation...
Quantification and comparison of the regional acceleratory phenomenon in bone following piezosurgery or bur osteotomy: A pilot study in rats.
Clinical and experimental dental research
Nov 11, 2022
BACKGROUND/OBJECTIVE: The Regional Acceleratory Phenomenon (RAP) can be induced surgically via decortication (selective cortical penetrations) of bone to accelerate orthodontic tooth movement. Few studies have compared the impact and efficiency of di...
Accurate prediction of histological grading of intraductal papillary mucinous neoplasia using deep learning.
Endoscopy
Nov 2, 2022
BACKGROUND: Risk stratification and recommendation for surgery for intraductal papillary mucinous neoplasm (IPMN) are currently based on consensus guidelines. Risk stratification from presurgery histology is only potentially decisive owing to the low...
Artificial intelligence using deep learning to predict the anatomical outcome of rhegmatogenous retinal detachment surgery: a pilot study.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Oct 28, 2022
PURPOSE: To develop and evaluate an automated deep learning model to predict the anatomical outcome of rhegmatogenous retinal detachment (RRD) surgery.
Machine learning for detecting centre-level irregularities in randomized controlled trials: A pilot study.
Contemporary clinical trials
Oct 15, 2022
Centralized statistical monitoring is sometimes employed as an alternative to onsite monitoring for randomized control trials. Current central monitoring methods have limitations, in that they are relatively resource intensive and do not necessarily ...
A novel assessment model for teaching robot-assisted living donor nephrectomy in abdominal transplant surgery fellowship.
American journal of surgery
Oct 4, 2022
BACKGROUND: An increasing number of transplant centers have adopted robot-assisted living donor nephrectomy. Thus, a transplant fellow assessment tool is needed for promoting operative independence in an objective and safe manner.
A deep learning-based approach to diagnose mild traumatic brain injury using audio classification.
PloS one
Sep 28, 2022
Mild traumatic brain injury (mTBI or concussion) is receiving increased attention due to the incidence in contact sports and limitations with subjective (pen and paper) diagnostic approaches. If an mTBI is undiagnosed and the athlete prematurely retu...
In a pilot study, automated real-time systematic review updates were feasible, accurate, and work-saving.
Journal of clinical epidemiology
Sep 20, 2022
OBJECTIVES: The aim of this study is to describe and pilot a novel method for continuously identifying newly published trials relevant to a systematic review, enabled by combining artificial intelligence (AI) with human expertise.
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