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Sequencing validates deep learning models for EHR-based detection of Noonan syndrome in pediatric patients
United States · Sequencing validates deep learning models for EHR-based detection of Noonan syndrome in pediatric patients - United StatesDespite advanced diagnostic tools, early detection of rare genetic conditions like Noonan syndrome (NS) remains challenging. We evaluated a deep learning model’s real-world performance in identifying potential NS cases using electronic health record (EHR) data, validated through genetic sequencing and clinical assessment. The model analyzed 92,428 patients, identifying 171 high-risk individuals (score > 0.8) who underwent comprehensive review. Among these, 86 had prior genetic diagnoses, including three NS cases diagnosed during the study period. Genetic sequencing of remaining patients identified two additional NS cases with pathogenic variants. The model achieved 2.92% precision and 99.82% specificity. While precision was lower than prior validation (33.3%), this reflected expected differences in disease prevalence rather than model degradation. NS-associated phenotypes were enriched among high-risk patients, and trajectory analysis showed potential for earlier identification, highlighting both promise and limitations of EHR-based computational screening tools.
An end-to-end multifunctional AI platform for intraoperative diagnosis
United States · An end-to-end multifunctional AI platform for intraoperative diagnosis - United StatesIntraoperative frozen section diagnosis provides essential, real-time histological insights to guide surgical decisions. However, the quality of these time-sensitive sections is often suboptimal, posing significant diagnostic challenges for pathologists. To address these limitations, we utilized over 6700 whole slide images to develop GAS, a comprehensive platform comprising three modules: Generation, Assessment, and Support modules. The Generation module, based on a GAN-driven multimodal network guided by FFPE-style text descriptions, demonstrated effective enhancement of frozen section quality across various organs. The Assessment module, which fine-tuned quality control models using pathological foundation models, showed substantial improvements in microstructural quality for the generated images. Validated through a prospective study (ChiCTR2300076555) on the human–AI collaboration software, the Support module demonstrated that GAS significantly boosted diagnostic confidence for pathologists. In summary, this study highlights the clinical utility of the GAS platform in intraoperative diagnosis and establishes a new paradigm for integrating end-to-end AI solutions into clinical workflows.

Incorporating genetic data improves target trial emulations and informs the use of polygenic scores in randomized controlled trial design

Ethics statement This study complies with all relevant ethical regulations and was conducted in accordanc…
#NewsBeep #News #Genetics #Agriculture #AnimalGeneticsandGenomics #Biomedicine #CancerResearch #Epidemiology #GeneFunction #general #Geneticsresearch #HumanGenetics #Randomizedcontrolledtrials #Science #UK #UnitedKingdom
newsbeep.com/uk/9054/

Decomposition of phenotypic heterogeneity in autism reveals underlying genetic programs

Identifying the structures of autism phenotypes To best reflect the complexity of presentations across autistic individuals, we identified…
#NewsBeep #News #Genetics #Agriculture #AnimalGeneticsandGenomics #Autismspectrumdisorders #Biomedicine #CancerResearch #GeneFunction #general #Geneticsresearch #HumanGenetics #Science #UK #UnitedKingdom
newsbeep.com/uk/8820/

Mapping trait-associated cells with spatial transcriptomics

Genome-wide association studies (GWAS) have uncovered numerous genetic variants associated with complex traits. However, a critical gap remains…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Genetics #Agriculture #AnimalGeneticsandGenomics #Biomedicine #CancerResearch #GeneExpression #GeneFunction #general #HumanGenetics #Science #Statisticalmethods
newsbeep.com/us/13537/

Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from T2-FLAIR: a multi-centre study

Patient enrolment and baseline characteristics A total of 783 su…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Artificialintelligence #AI #ArtificialIntelligence #Biomedicine #biotechnology #Cognitiveageing #Cognitiveneuroscience #Computationalneuroscience #general #Imageprocessing #Medicine/PublicHealth #Technology
newsbeep.com/us/12439/