首页|University of Kentucky College of Medicine Reports Findings in Artificial Intell igence (Utility of artificial intelligence in a binary classification of soft ti ssue tumors)
University of Kentucky College of Medicine Reports Findings in Artificial Intell igence (Utility of artificial intelligence in a binary classification of soft ti ssue tumors)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Lexington,Kentuc ky,by NewsRx journalists,research stated,"Soft tissue tumors (STTs) pose diag nostic and therapeutic challenges due to their rarity,complexity,and morpholog ical overlap. Accurate differentiation between benign and malignant STTs is impo rtant to set treatment directions,however,this task can be difficult." The news correspondents obtained a quote from the research from the University o f Kentucky College of Medicine,"The integration of machine learning and artific ial intelligence (AI) models can potentially be helpful in classifying these tum ors. The aim of this study was to investigate AI and machine learning tools in t he classification of STT into benign and malignant categories. This study consis ted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologis ts from different medical centers independently reviewed 100 WSIs,representing 100 different cases,with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPa th) and a cohort of 95 cases,several cell-specific parameters were found to be statistically significant,most notably cell count,nucleus/cell area ratio,nuc leus hematoxylin density mean,and cell max caliper. (3) Evaluation of machine l earning library (Scikit-learn) in differentiating benign and malignant STTs. A t otal of 195 STT cases (156 cases in the training group and 39 cases in the valid ation group) achieved approximately 70% sensitivity and specificit y,and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and ident ify parameters that can differentiate between benign and malignant STTs."
LexingtonKentuckyUnited StatesNort h and Central AmericaArtificial IntelligenceCyborgsDiagnostics and Screeni ngEmerging TechnologiesHealth and MedicineMachine Learning
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