Comparison of Semi|Quantitative Scoring and Artificial Intelligence ...

Comparison of Semi-Quantitative Scoring and Artificial Intelligence ...

Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical ...

Artificial intelligence-driven mobile interpretation of a semi ...

These scores were then compared, and any discrepancies were noted. ... Semi-Quantitative (CrAgSQ) score for both visualization and AI readings.

Comparison of machine learning and semi-quantification algorithms ...

Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning ...

Comparison of machine learning and semi-quantification algorithms ...

The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions: ...

Development and technical validation of an artificial intelligence ...

The performance of the AI model for all cell type features was similar/non-inferior to that of our group of GI pathologists (F1-scores: 94.5–94.8 for AI vs ...

Clinical validation of an AI-based pathology tool for scoring ... - Nature

... quantitative scores. Am. J. Clin. Pathol. 147, 364–369 ... Comparison of manual vs machine learning approaches to liver biopsy scoring ...

How Good Is That AI-Penned Radiology Report?

The analysis showed that compared with human radiologists, automated scoring systems fared worse in their ability to evaluate the AI-generated ...

Qualitopix: Artificial intelligence-based quantitative quality ...

... scores (0-100). Outliers were determined at 1 standard deviation. a selection of outliers was rescanned. Inter- scanner comparisons and ...

Real-Time Artificial Intelligence–Based Guidance of ...

Quantitative analysis was feasible in 83% images acquired by novices and resulted in high correlations (r≥0.74) and small biases, compared with ...

APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for ...

Most studies were moderate quality. The 5 lowest scoring items included source of data, sample size calculation, bias assessment, error analysis ...

Artificial Intelligence Enables Quantitative Assessment of Ulcerative ...

001) when compared with pathologist consensus Nancy histological index scores. We were also able to predict histologic remission, based on the absence of ...

Artificial intelligence-based assessment of PD-L1 expression in ...

The tumor proportion score (TPS) calculates the proportion of tumor cells in IHC, which is a key quantitative indicator reflecting PD-L1 ...

Artificial intelligence-based quantitative coronary angiography of ...

AI-QCA detected 995 out of the 1115 lesions identified by manual QCA (sensitivity = 89%). Among 995 matched lesions, the DS difference between ...

Artificial intelligence for tumour tissue detection and histological ...

Comparison of the regression grading provided by the AI tool with the gradings of 12 board-certified pathologists was done for 95 full cases (1407 slides; ...

Quantitative Evaluation of Machine Learning Explanations

The difference in scores was mainly due to the clear non-uniform distribution of feature importance in human attention masks while the segmentation mask weights ...

Artificial Intelligence–based Coronary Stenosis Quantification at ...

Blinded comparisons between QCA and AI-CSQ software were conducted at the QCA-defined MLD and at the AI-CSQ–defined MLD within a full QCA- ...

Automated Interpretation of Clinical Electroencephalograms Using ...

SCORE-AI demonstrated substantially greater specificity compared with the previously published models (90% vs 3%-63%) and was more specific than ...

Comparison of FDA-Approved vs. Mayo Clinic's Ki-67 ...

Mayo Clinic's Ki-67 Immunostaining Protocols for Breast Cancer Specimens: A Quantitative Analysis Using Artificial Intelligence (AI) Based Image ...

Artificial intelligence-assisted system for precision diagnosis of PD ...

The TPS comparison test in the 22c3 assay showed strong consistency between the TPS calculated with the AI system and trained pathologists (R = 0.9429–0.9458).

Quantitative Evaluation of Three Methodological Pitfalls | Radiology

Machine learning models developed with these methodological pitfalls, which are undetectable during internal evaluation, produce inaccurate ...