Kevin Chen
@kchen315
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Surgery resident @UNCSurgery, applying machine learning to colorectal surgery, tweets are my own
Joined January 2017
Overjoyed to have matched at Cleveland Clinic for colorectal fellowship! Forever grateful to @UNCSurgery for training and mentoring me, especially @muneerakapadia
Another outstanding match day for @ClevelandClinic Colorectal Surgery department! 6 up and coming surgeons that will immediately start making a difference in our speciality. @DavidLiskaMD @ScottRSteeleMD @arikanters @MRegueiroMD @EmreGorgunMD @HolubarStefan
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Our recent publication uses transcriptomics to analyze the overlap in differentially expressed genes and gene pathways between post-operative Crohn's disease recurrence and surgical site infections to suggest underlying biological mechanisms https://t.co/3YYBL0kBth
link.springer.com
Digestive Diseases and Sciences - Crohn’s disease (CD) is a chronic inflammatory condition affecting the gastrointestinal tract, characterized by complications such as strictures, fistulas,...
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Our recent editorial in DCR highlights a project opening a new frontier in AI-assisted intra-op support and emphasizes the need for surgeon-owned, open-source data and models. https://t.co/hZeCJqjLzJ
@patsyllamd
journals.lww.com
An abstract is unavailable.
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Our recent publication in DCR developed a machine learning-based model for predicting pathologic complete response for rectal cancer. Future models could help guide patient selection for non-operative management. https://t.co/hT5UsygTGd
@muneerakapadia @shawnmgomez
pmc.ncbi.nlm.nih.gov
Pathologic complete response after neoadjuvant therapy is an important prognostic indicator for locally advanced rectal cancer and may give insights into which patients might be treated non-operati...
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Our recent publication shows the potential for machine learning applied to gene expression (RNASeq) to predict clinical outcomes in pediatric Crohn's disease https://t.co/xJAzQEhzTd
pmc.ncbi.nlm.nih.gov
Pediatric Crohn’s disease (CD) is characterized by a severe disease course with frequent complications. We sought to apply machine learning-based models to predict risk of developing future complic...
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We developed an AI model which can use intra-op specimen mammography to predict the pathologic margin status of partial mastectomy specimens with accuracy equal to surgeons and radiologists. @Dr_KGallagher @shawnmgomez
https://t.co/rY02054J7r
link.springer.com
Annals of Surgical Oncology - Intraoperative specimen mammography is a valuable tool in breast cancer surgery, providing immediate assessment of margins for a resected tumor. However, the accuracy...
While some cancers can be seen or felt, others may microscopic. 🔬 A new #AI tool, developed by researchers at @UNCSurgery, @jointbme, and @UNC_Lineberger, will allow #surgeons to more accurately analyze and remove tumors during #BreastCancer surgery. https://t.co/NJJGJMg9mK
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Our letter to the editor in JACS proposes that an ML-based NSQIP risk calculator should be: 1. Procedure-specific 2. Interpretable 3. Open-source and collaboratively developed https://t.co/ERqHHyuAwP
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ACS NSQIP Risk Calculator Accuracy Using a Machine Learning Algorithm Compared to Regression https://t.co/KWHlyGNjrd
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The Department of Surgery lost a cherished member of the team this week after a long illness. Katie Iles joined us as a resident in 2018. We grieve her loss and she will be deeply missed, but we will remember her and the contributions she made to our department.
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I don’t usually weigh in on non-neurology debates but thought I would fix a typo in this title
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A radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer, validated in 3 external cohorts https://t.co/7xwljt0s36
pubmed.ncbi.nlm.nih.gov
National Natural Science Foundation of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences.
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Our recent publication presents machine learning-based models that can more accurately predict readmission after colorectal surgery, showing an important role for AI in quality improvement. https://t.co/IQnmPQauCE
@muneerakapadia @shawnmgomez
pubmed.ncbi.nlm.nih.gov
Machine learning approaches outperformed traditional statistical methods in the prediction of readmission after colorectal surgery. After external validation, this improved prediction model could be...
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Clinical decision support (CDS) have the potential to "support surgeons’ delivery of high-quality care and reduce physician burden" by applying AI to patient outcomes https://t.co/s8o0fVBacq
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A multi-institutional study shows improved detection rates for AI-assisted reading of capsule endoscopy recordings in 10% of the time.
jamanetwork.com
This diagnostic study develops and evaluates the performance of a convolutional neural network algorithm for review of small bowel capsule endoscopy video in clinical care.
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A Smartphone Application Using Artificial Intelligence Is Superior To Subject Self-Reporting When Assessing Stool Form https://t.co/WswKrat2IS
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Algorithm-based care versus usual care for the early recognition and management of complications after pancreatic resection in the Netherlands: an open-label, nationwide, stepped-wedge cluster-randomised trial https://t.co/39CLw0IR41
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Our publication in Journal of GI Surgery shows improved performance for machine learning in predicting procedure-specific outcomes @muneerakapadia @shawnmgomez
https://t.co/uZLnZfzrcL
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Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation https://t.co/XUcEKYDMWV
nature.com
npj Digital Medicine - Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation
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Machine Learning Improves Prediction Over Logistic Regression on Resected Colon Cancer Patients https://t.co/wzs8Ygv2yt
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