chore(openchallenges): 2024-03-01 DB update (Sage-Bionetworks#2539)

Co-authored-by: vpchung <[email protected]>

apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv

@@ -181,7 +181,7 @@
 "180","lish-moa","Mechanisms of Action (MoA) Prediction","Improve the algorithm that classifies drugs based on their biological activity","Can you improve the algorithm that classifies drugs based on their biological activity?","","https://www.kaggle.com/competitions/lish-moa","completed","8","","2020-09-03","2020-11-30","2023-08-08 19:09:31","2023-11-14 19:33:49"
 "181","recursion-cellular-image-classification","Recursion Cellular Image Classification","CellSignal-Disentangling biological signal in cellular images","This competition will have you disentangling experimental noise from real biological signals. Your entry will classify images of cells under one of 1,108 different genetic perturbations. You can help eliminate the noise introduced by technical execution and environmental variation between experiments. If successful, you could dramatically improve the industry’s ability to model cellular images according to their relevant biology. In turn, applying AI could greatly decrease the cost of treatments, and ensure these treatments get to patients faster.","","https://www.kaggle.com/competitions/recursion-cellular-image-classification","completed","8","","2019-06-27","2019-09-26","2023-08-08 19:38:42","2023-11-14 19:34:11"
 "182","tlvmc-parkinsons-freezing-gait-prediction","Parkinson's Freezing of Gait Prediction","Event detection from wearable sensor data","The goal of this competition is to detect freezing of gait (FOG), a debilitating symptom that afflicts many people with Parkinson’s disease. You will develop a machine learning model trained on data collected from a wearable 3D lower back sensor. Your work will help researchers better understand when and why FOG episodes occur. This will improve the ability of medical professionals to optimally evaluate, monitor, and ultimately, prevent FOG events.","","https://www.kaggle.com/competitions/tlvmc-parkinsons-freezing-gait-prediction","completed","8","","2023-03-09","2023-06-08","2023-08-08 19:47:54","2023-10-10 19:53:08"
-"183","chaimeleon","CHAIMELEON Open Challenges","AI-powered solutions driving innovation in cancer diagnosis and treatment","The CHAIMELEON Open Challenges is a competition designed to train and refine AI models to answer clinical questions about five types of cancer-prostate, lung, breast, colon, and rectal. Participants are challenged to collaborate and develop innovative AI-powered solutions that can significantly impact cancer diagnosis, management, and treatment. They will be evaluated considering a balance between the performance of their AI algorithms to predict different clinical endpoints such as disease staging, treatment response or progression free survival and their trustworthiness.  The challenges are open to the whole scientific and tech community interested in AI. They are a unique opportunity to showcase how AI can be used to advance medical research and improve patient outcomes within the CHAIMELEON project.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/744/Logo_Grand_Challenge_-_2.png","https://chaimeleon.grand-challenge.org/","active","5","","2023-11-02","2024-02-29","2023-08-09 17:13:09","2024-02-26 19:18:26"
+"183","chaimeleon","CHAIMELEON Open Challenges","AI-powered solutions driving innovation in cancer diagnosis and treatment","The CHAIMELEON Open Challenges is a competition designed to train and refine AI models to answer clinical questions about five types of cancer-prostate, lung, breast, colon, and rectal. Participants are challenged to collaborate and develop innovative AI-powered solutions that can significantly impact cancer diagnosis, management, and treatment. They will be evaluated considering a balance between the performance of their AI algorithms to predict different clinical endpoints such as disease staging, treatment response or progression free survival and their trustworthiness.  The challenges are open to the whole scientific and tech community interested in AI. They are a unique opportunity to showcase how AI can be used to advance medical research and improve patient outcomes within the CHAIMELEON project.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/744/Logo_Grand_Challenge_-_2.png","https://chaimeleon.grand-challenge.org/","completed","5","","2023-11-02","2024-02-29","2023-08-09 17:13:09","2024-02-26 19:18:26"
 "184","topcow23","Topology-Aware Anatomical Segmentation of the Circle of Willis","Segment the Circle of Willis (CoW) vessel components for both CTA and MRA","The aim of the challenge is to extract the CoW angio-architecture from 3D angiographic imaging by segmentation of the vessel components. There are two sub-tasks-binary segmentation of CoW vessels, and multi-class CoW anatomical segmentation. We release a new dataset of joint-modalities, CTA and MRA of the same patient cohort, both with annotations of the anatomy of CoW. Our challenge has two tracks for the same segmentation task, namely CTA track and MRA track. We made use of the clinical information from both modalities during our annotation. And participants can pick whichever modality they want, both CTA and MRA, and choose to tackle the task for either modality.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/733/TopCow_logo.jpg","https://topcow23.grand-challenge.org/","completed","5","","2023-08-20","2023-09-25","2023-08-09 17:16:22","2024-01-31 22:42:32"
 "185","circle-of-willis-intracranial-artery-classification-and-quantification-challenge-2023","Circle of Willis Intracranial Artery Classification and Quantification Challenge 2023","Classify the circle of Willis (CoW) configuration and quantification","The purpose of this challenge is to compare automatic methods for classification of the circle of Willis (CoW) configuration and quantification of the CoW major artery diameters and bifurcation angles.","","https://crown.isi.uu.nl/","completed","\N","","2023-05-01","2023-08-15","2023-08-09 22:13:24","2023-09-28 23:24:54"
 "186","making-sense-of-electronic-health-record-ehr-race-and-ethnicity-data","Making Sense of Electronic Health Record (EHR) Race and Ethnicity Data","Make sense of electronic health record race and ethnicity data","The urgency of the coronavirus disease 2019 (COVID-19) pandemic has heightened interest in the use of real-world data (RWD) to obtain timely information about patients and populations and has focused attention on EHRs. The pandemic has also heightened awareness of long-standing racial and ethnic health disparities along a continuum from underlying social determinants of health, exposure to risk, access to insurance and care, quality of care, and responses to treatments. This highlighted the potential that EHRs can be used to describe and contribute to our understanding of racial and ethnic health disparities and their solutions. The OMB Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity provides minimum standards for maintaining, collecting, and presenting data on race and ethnicity for all Federal reporting purposes, and defines the two separate constructs of race and ethnicity.","","https://precision.fda.gov/challenges/30","completed","6","","2023-05-31","2023-06-23","2023-08-10 18:28:06","2023-11-14 19:34:58"