chore(openchallenges): 2024-07-11 DB update (#2738)

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

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

@@ -503,7 +503,7 @@
 "502","precisionfda-automated-machine-learning-automl-app-a-thon","precisionFDA Automated Machine Learning (AutoML) App-a-thon","Unlock new insights into its potential applications in healthcare and medicine","Say goodbye to the days when machine learning (ML) access was the exclusive purview of data scientists and hello to automated ML (AutoML), a low-code ML technique designed to empower professionals without a data science background and enable their access to ML. Although ML and artificial intelligence (AI) have been highly discussed topics in healthcare and medicine, only 15% of hospitals are routinely using ML due to lack of ML expertise and a lengthy data provisioning process. Can AutoML help bridge this gap and expand ML throughout healthcare?  The goal of this app-a-thon is to evaluate the effectiveness of AutoML when applied to biomedical datasets. This app-a-thon aligns with the new Executive Order on Safe, Secure, and Trustworthy Development and Use of AI, which calls for agencies to promote competition in AI. The results of this app-a-thon will be used to help inform regulatory science by evaluating whether AutoML can match or improve the performance of traditional, human-c...","","https://precision.fda.gov/challenges/32","completed","6","","2024-02-26","2024-04-26","\N","2024-03-11 22:58:43","2024-03-11 23:02:12"
 "503","dream-olfactory-mixtures-prediction","DREAM olfactory mixtures prediction","Predicting smell from molecule features","The goal of the DREAM Olfaction Challenge is to find models that can predict how close two mixtures of molecules are in the odor perceptual space (on a 0-1 scale, 0 is total overlap, 1 is the furthest away) using physical and chemical features. For this challenge, we are providing a large published training-set of 500 mixtures measurements obtained from 3 publications, mixtures have varying number of molecules and an unpublished test-set of 46 equi-intense mixtures of 10 molecules whose distance was rated by 35 human subjects.","","https://www.synapse.org/#!Synapse:syn53470621/wiki/626022","active","1","","2024-04-19","2024-08-01","2319","2024-04-22 18:21:54","2024-04-22 21:54:39"
 "504","fets-2024","Federated Tumor Segmentation (FeTS) 2024 Challenge","Benchmarking weight aggregation methods for federated training","Contrary to previous years, this time we only focus on one task and invite participants to compete in “Federated Training” for effective weight aggregation methods for the creation of a consensus model given a pre-defined segmentation algorithm for training, while also (optionally) accounting for network outages.  The same data is used as in FeTS 2022 challenge, but this year the epmhasis is on instance segmentation of brain tumors.","","https://www.synapse.org/fets2024","completed","1","","2024-04-01","2024-07-01","\N","2024-04-22 22:07:18","2024-04-22 22:07:18"
-"505","mario","🕹️ 🍄 MARIO : Monitoring AMD progression in OCT","Improve the planning of anti-VEGF treatments","Age-related Macular Degeneration (AMD) is a progressive degeneration of the macula, the central part of the retina, affecting nearly 196 million people worldwide 1. It can appear from the age of 50, and more frequently from the age of 65 onwards, causing a significant weakening of visual capacities, without destroying them. It is a complex and multifactorial pathology in which genetic and environmental risk factors are intertwined. Advanced stages of the disease (atrophy and neovascularization) affect nearly 20% of patients: they are the first cause of severe visual impairment and blindness in developed countries. Since their introduction in 2007, Anti–vascular endothelial growth factor (anti-VEGF) treatments have proven their ability to slow disease progression and even improve visual function in neovascular forms of AMD 2. This effectiveness is optimized by ensuring a short time between the diagnosis of the pathology and the start of treatment as well as by performing regular ch...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/666/square_image_mario_t8tUYoc.png","https://www.codabench.org/competitions/2851/","active","10","","2024-04-01","2024-07-10","\N","2024-04-29 18:13:15","2024-05-10 16:48:04"
+"505","mario","🕹️ 🍄 MARIO : Monitoring AMD progression in OCT","Improve the planning of anti-VEGF treatments","Age-related Macular Degeneration (AMD) is a progressive degeneration of the macula, the central part of the retina, affecting nearly 196 million people worldwide 1. It can appear from the age of 50, and more frequently from the age of 65 onwards, causing a significant weakening of visual capacities, without destroying them. It is a complex and multifactorial pathology in which genetic and environmental risk factors are intertwined. Advanced stages of the disease (atrophy and neovascularization) affect nearly 20% of patients: they are the first cause of severe visual impairment and blindness in developed countries. Since their introduction in 2007, Anti–vascular endothelial growth factor (anti-VEGF) treatments have proven their ability to slow disease progression and even improve visual function in neovascular forms of AMD 2. This effectiveness is optimized by ensuring a short time between the diagnosis of the pathology and the start of treatment as well as by performing regular ch...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/666/square_image_mario_t8tUYoc.png","https://www.codabench.org/competitions/2851/","active","10","","2024-04-01","2024-07-30","\N","2024-04-29 18:13:15","2024-07-11 21:53:02"
 "506","hntsmrg24","Head and Neck Tumor Segmentation for MR-Guided Applications","Head and Neck Tumor Segmentation","This challenge focuses on developing algorithms to automatically segment head and neck cancer gross tumor volumes on multi-timepoint MRI","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/745/logo_v0.png","https://hntsmrg24.grand-challenge.org/","active","5","","2024-05-01","2024-09-15","\N","2024-04-29 18:15:37","2024-05-20 16:37:46"
 "507","acouslic-ai","Abdominal Circumference Operator-agnostic UltraSound measurement","Fetal growth restriction prediction","Fetal growth restriction (FGR), affecting up to 10% of pregnancies, is a critical factor contributing to perinatal morbidity and mortality (1-3). Strongly linked to stillbirths, FGR can also lead to preterm labor, posing risks to the mother (4,5). This condition often results from an impediment to the fetus' genetic growth potential due to various maternal, fetal, and placental factors (6). Measurements of the fetal abdominal circumference (AC) as seen on prenatal ultrasound are a key aspect of monitoring fetal growth. When smaller than expected, these measurements can be indicative of FGR, a condition linked to approximately 60% of fetal deaths (4). FGR diagnosis relies on repeated measurements of either the fetal abdominal circumference (AC), the expected fetal weight, or both. These measurements must be taken at least twice, with a minimum interval of two weeks between them for a reliable diagnosis (7). Additionally, an AC measurement that falls below the third percentile is, b...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/753/acouslicai-logo_tjZmpqL.png","https://acouslic-ai.grand-challenge.org/","active","5","","2024-05-05","2024-07-31","\N","2024-04-29 18:21:37","2024-05-20 16:38:17"
 "508","leopard","The LEOPARD Challenge","Uncover finer morphological features' prognostic value","Recently, deep learning was shown (H. Pinckaers et al., 2022; O. Eminaga et. al., 2024) to be able to predict the biochemical recurrence of prostate cancer.  Hypothesizing that deep learning could uncover finer morphological features' prognostic value, we are organizing the LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes (LEOPARD) challenge. The goal of this challenge is to yield top-performance deep learning solutions to predict the time to biochemical recurrence from H&E-stained histopathological tissue sections, i.e. based on morphological features.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/754/logo.png","https://leopard.grand-challenge.org/","active","5","","2024-04-10","2024-08-01","\N","2024-04-29 18:28:44","2024-05-20 16:38:34"