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Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks.

ABSTRACT: Stone artifacts are often the most abundant class of objects found in archaeological sites but their consistent identification is limited by the number of experienced analysts available. We report a machine learning based technology for stone artifact identification as part of a solution to the lack of such experts directed at distinguishing worked stone objects from naturally occurring lithic clasts. Three case study locations from Egypt, Australia, and New Zealand provide a data set of 6769 2D images, 3868 flaked artifact and 2901 rock images used to train and test a machine learning model based on an openly available PyTorch implementation of Faster R-CNN ResNet 50. Results indicate 100% agreement between the model and original human derived classifications, a better performance than the results achieved independently by two human analysts who reassessed the 2D images available to the machine learning model. Machine learning neural networks provide the potential to consistently assess the composition of large archaeological assemblages composed of objects modified in a variety of ways.

SUBMITTER: Emmitt J

PROVIDER: S-EPMC9365149 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks.

Emmitt Joshua J Masoud-Ansari Sina S Phillipps Rebecca R Middleton Stacey S Graydon Jennifer J Holdaway Simon S

PloS one 20220810 8

Stone artifacts are often the most abundant class of objects found in archaeological sites but their consistent identification is limited by the number of experienced analysts available. We report a machine learning based technology for stone artifact identification as part of a solution to the lack of such experts directed at distinguishing worked stone objects from naturally occurring lithic clasts. Three case study locations from Egypt, Australia, and New Zealand provide a data set of 6769 2D ...[more]

PMID: 35947537

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Project description:PurposeTo investigate the diagnostic accuracy of cascading convolutional neural network (CNN) for urinary stone detection on unenhanced CT images and to evaluate the performance of pretrained models enriched with labeled CT images across different scanners.Materials and methodsThis HIPAA-compliant, institutional review board-approved, retrospective clinical study used unenhanced abdominopelvic CT scans from 535 adults suspected of having urolithiasis. The scans were obtained on two scanners (scanner 1 [hereafter S1] and scanner 2 [hereafter S2]). A radiologist reviewed clinical reports and labeled cases for determination of reference standard. Stones were present on 279 (S1, 131; S2, 148) and absent on 256 (S1, 158; S2, 98) scans. One hundred scans (50 from each scanner) were randomly reserved as the test dataset, and the rest were used for developing a cascade of two CNNs: The first CNN identified the extent of the urinary tract, and the second CNN detected presence of stone. Nine variations of models were developed through the combination of different training data sources (S1, S2, or both [hereafter SB]) with (ImageNet, GrayNet) and without (Random) pretrained CNNs. First, models were compared for generalizability at the section level. Second, models were assessed by using area under the receiver operating characteristic curve (AUC) and accuracy at the patient level with test dataset from both scanners (n = 100).ResultsThe GrayNet-pretrained model showed higher classifier exactness than did ImageNet-pretrained or Random-initialized models when tested by using data from the same or different scanners at section level. At the patient level, the AUC for stone detection was 0.92-0.95, depending on the model. Accuracy of GrayNet-SB (95%) was higher than that of ImageNet-SB (91%) and Random-SB (88%). For stones larger than 4 mm, all models showed similar performance (false-negative results: two of 34). For stones smaller than 4 mm, the number of false-negative results for GrayNet-SB, ImageNet-SB, and Random-SB were one of 16, three of 16, and five of 16, respectively. GrayNet-SB identified stones in all 22 test cases that had obstructive uropathy.ConclusionA cascading model of CNNs can detect urinary tract stones on unenhanced CT scans with a high accuracy (AUC, 0.954). Performance and generalization of CNNs across scanners can be enhanced by using transfer learning with datasets enriched with labeled medical images.© RSNA, 2019Supplemental material is available for this article. : An earlier incorrect version appeared online. This article was corrected on August 6, 2019.

Project description:BackgroundMachine learning (ML) achieves better predictions of postoperative mortality than previous prediction tools. Free-text descriptions of the preoperative diagnosis and the planned procedure are available preoperatively. Because reading these descriptions helps anesthesiologists evaluate the risk of the surgery, we hypothesized that deep learning (DL) models with unstructured text could improve postoperative mortality prediction. However, it is challenging to extract meaningful concept embeddings from this unstructured clinical text.ObjectiveThis study aims to develop a fusion DL model containing structured and unstructured features to predict the in-hospital 30-day postoperative mortality before surgery. ML models for predicting postoperative mortality using preoperative data with or without free clinical text were assessed.MethodsWe retrospectively collected preoperative anesthesia assessments, surgical information, and discharge summaries of patients undergoing general and neuraxial anesthesia from electronic health records (EHRs) from 2016 to 2020. We first compared the deep neural network (DNN) with other models using the same input features to demonstrate effectiveness. Then, we combined the DNN model with bidirectional encoder representations from transformers (BERT) to extract information from clinical texts. The effects of adding text information on the model performance were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Statistical significance was evaluated using P<.05.ResultsThe final cohort contained 121,313 patients who underwent surgeries. A total of 1562 (1.29%) patients died within 30 days of surgery. Our BERT-DNN model achieved the highest AUROC (0.964, 95% CI 0.961-0.967) and AUPRC (0.336, 95% CI 0.276-0.402). The AUROC of the BERT-DNN was significantly higher compared to logistic regression (AUROC=0.952, 95% CI 0.949-0.955) and the American Society of Anesthesiologist Physical Status (ASAPS AUROC=0.892, 95% CI 0.887-0.896) but not significantly higher compared to the DNN (AUROC=0.959, 95% CI 0.956-0.962) and the random forest (AUROC=0.961, 95% CI 0.958-0.964). The AUPRC of the BERT-DNN was significantly higher compared to the DNN (AUPRC=0.319, 95% CI 0.260-0.384), the random forest (AUPRC=0.296, 95% CI 0.239-0.360), logistic regression (AUPRC=0.276, 95% CI 0.220-0.339), and the ASAPS (AUPRC=0.149, 95% CI 0.107-0.203).ConclusionsOur BERT-DNN model has an AUPRC significantly higher compared to previously proposed models using no text and an AUROC significantly higher compared to logistic regression and the ASAPS. This technique helps identify patients with higher risk from the surgical description text in EHRs.

Dataset Information

Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks.

Publications

Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks.

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