** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** RECENT FINDINGS FROM OUR ON METRITIS SPONTANEOUS CURE (MSC) USING DEEP ARTIFICIAL NEURAL NETWORKS (ANN) CORRECTLY IDENTIFIED 80% OF THE COWS THAT EXPERIENCED A SPONTANEOUS CURE AND 76% OF ALL COWS THAT CURED OR FAILED TO CURE FROM METRITIS. ALBEIT THESE RESULTS WERE HIGHLY ENCOURAGING, AN EXTERNAL VALIDATION, A COMPREHENSIVE ECONOMIC ANALYSIS, AND AN ASSESSMENT OF OTHER ALREADY COMMERCIALLY AVAILABLE TECHNOLOGIES, SUCH AS COW GENOMICS AND COW SENSOR BEHAVIORAL DATA, WOULD BE CRITICAL TO OPTIMIZE PREDICTIVE MODELS AND SOLIDIFY THE EVIDENCE FOR THE IMPLEMENTATION OF SELECTIVE THERAPY FOR METRITIS AND REDUCE AMU IN DAIRY COWS IN THE VARIOUS DAIRY SCENARIOS. FURTHERMORE, METRITIS IS A POLYMICROBIAL DISEASE IN WHICH COWS FAILING TO CURE METRITIS HAVE AN INCREASED ABUNDANCE OF GENES ASSOCIATED WITH LPS SYNTHESIS (GALE) AND QUORUM SENSING SIGNALING (PGI)THAT MIGHT CONTRIBUTE TO MICROBES INTERACTIONS ASSOCIATED WITH METRITIS CURE FAILURE.THEREFORE, THERE IS A CRITICAL NEED TO EXTERNALLY VALIDATE OUR MSC DEEP ANN PREDICTIVE MODEL'S COST-EFFECTIVENESS AND EVALUATE IF THE INTEGRATION OF MICROBIAL AND HOST GENOMICS AND COW SENSOR BEHAVIORAL DATA CAN IMPROVE MSC PREDICTABILITY AND COST-EFFECTIVENESS. OUR LONG-TERM GOAL IS TO DEVELOP HIGHLY ACCURATE ARTIFICIAL INTELLIGENCE-DRIVEN MODELSTHAT PREDICT METRITIS CURE IN DAIRY COWS IN DIVERSE SCENARIOS AND REDUCE ANTIMICROBIAL USE WITHOUT COMPROMISING ANIMAL WELFARE AND THE COST-EFFECTIVENESS OF DAIRY FARMS. OUR CENTRAL HYPOTHESIS IS THAT THE INTEGRATION OF HOST AND MICROBIAL GENOMICS AND COW'S SENSOR BEHAVIORAL DATA WILL IMPROVE MSC PREDICTABILITY METRITIS AND ALLOW THE DEVELOPMENT OF SELECTIVE THERAPY THAT IS SAFE AND COST-EFFECTIVE IN A VARIETY OF SCENARIOS THAT REPRESENT THE DAIRY LANDSCAPE IN THE US AND BEYOND.