Artificial Intelligence-Based Triaging of Cutaneous Melanocytic Lesions
- URL: http://arxiv.org/abs/2410.10509v1
- Date: Mon, 14 Oct 2024 13:49:04 GMT
- Title: Artificial Intelligence-Based Triaging of Cutaneous Melanocytic Lesions
- Authors: Ruben T. Lucassen, Nikolas Stathonikos, Gerben E. Breimer, Mitko Veta, Willeke A. M. Blokx,
- Abstract summary: Pathologists are facing an increasing workload due to a growing volume of cases and the need for more comprehensive diagnoses.
We developed an artificial intelligence (AI) model for triaging cutaneous melanocytic lesions based on whole slide images.
- Score: 0.8864540224289991
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Pathologists are facing an increasing workload due to a growing volume of cases and the need for more comprehensive diagnoses. Aiming to facilitate workload reduction and faster turnaround times, we developed an artificial intelligence (AI) model for triaging cutaneous melanocytic lesions based on whole slide images. The AI model was developed and validated using a retrospective cohort from the UMC Utrecht. The dataset consisted of 52,202 whole slide images from 27,167 unique specimens, acquired from 20,707 patients. Specimens with only common nevi were assigned to the low complexity category (86.6%). In contrast, specimens with any other melanocytic lesion subtype, including non-common nevi, melanocytomas, and melanomas, were assigned to the high complexity category (13.4%). The dataset was split on patient level into a development set (80%) and test sets (20%) for independent evaluation. Predictive performance was primarily measured using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). A simulation experiment was performed to study the effect of implementing AI-based triaging in the clinic. The AI model reached an AUROC of 0.966 (95% CI, 0.960-0.972) and an AUPRC of 0.857 (95% CI, 0.836-0.877) on the in-distribution test set, and an AUROC of 0.899 (95% CI, 0.860-0.934) and an AUPRC of 0.498 (95% CI, 0.360-0.639) on the out-of-distribution test set. In the simulation experiment, using random case assignment as baseline, AI-based triaging prevented an average of 43.9 (95% CI, 36-55) initial examinations of high complexity cases by general pathologists for every 500 cases. In conclusion, the AI model achieved a strong predictive performance in differentiating between cutaneous melanocytic lesions of high and low complexity. The improvement in workflow efficiency due to AI-based triaging could be substantial.
Related papers
- CRTRE: Causal Rule Generation with Target Trial Emulation Framework [47.2836994469923]
We introduce a novel method called causal rule generation with target trial emulation framework (CRTRE)
CRTRE applies randomize trial design principles to estimate the causal effect of association rules.
We then incorporate such association rules for the downstream applications such as prediction of disease onsets.
arXiv Detail & Related papers (2024-11-10T02:40:06Z) - Advanced Predictive Modeling for Enhanced Mortality Prediction in ICU Stroke Patients Using Clinical Data [0.0]
Stroke is second-leading cause of disability and death among adults.
Approximately 17 million people suffer from a stroke annually, with about 85% being ischemic strokes.
We developed a deep learning model to assess mortality risk and implemented several baseline machine learning models for comparison.
arXiv Detail & Related papers (2024-07-19T11:17:42Z) - Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray [86.38767955626179]
Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray.
The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
arXiv Detail & Related papers (2024-03-27T16:56:14Z) - Clinical Melanoma Diagnosis with Artificial Intelligence: Insights from
a Prospective Multicenter Study [1.2397589403129072]
AI has proven to be helpful for enhancing melanoma detection.
Existing studies are limited by low sample sizes, too homogenous datasets, or lack of inclusion of rare melanoma subtypes.
We assessed 'All Data are Ext' (ADAE), an established open-source algorithm for detecting melanomas, by comparing its diagnostic accuracy to that of dermatologists.
arXiv Detail & Related papers (2024-01-25T14:03:54Z) - A Generalizable Artificial Intelligence Model for COVID-19
Classification Task Using Chest X-ray Radiographs: Evaluated Over Four
Clinical Datasets with 15,097 Patients [6.209420804714487]
The generalizability of the trained model was retrospectively evaluated using four different real-world clinical datasets.
The AI model trained using a single-source clinical dataset achieved an AUC of 0.82 when applied to the internal temporal test set.
An AUC of 0.79 was achieved when applied to a multi-institutional COVID-19 dataset collected by the Medical Imaging and Data Resource Center.
arXiv Detail & Related papers (2022-10-04T04:12:13Z) - Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in
Artificial Intelligence [79.038671794961]
We launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution.
Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK.
arXiv Detail & Related papers (2021-11-18T00:43:41Z) - The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk
Screening by Eye-region Manifestations [59.48245489413308]
We developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras.
The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1.
arXiv Detail & Related papers (2021-09-18T02:28:01Z) - Deep learning-based COVID-19 pneumonia classification using chest CT
images: model generalizability [54.86482395312936]
Deep learning (DL) classification models were trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries.
We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split.
The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better.
arXiv Detail & Related papers (2021-02-18T21:14:52Z) - Clinical prediction system of complications among COVID-19 patients: a
development and validation retrospective multicentre study [0.3569980414613667]
We used data collected from 3,352 COVID-19 patient encounters admitted to 18 facilities between April 1 and April 30, 2020 in Abu Dhabi (AD), UAE.
Using data collected during the first 24 hours of admission, the machine learning-based prognostic system predicts the risk of developing any of seven complications during the hospital stay.
The system achieves good accuracy across all complications and both regions.
arXiv Detail & Related papers (2020-11-28T18:16:23Z) - CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors
and Efficient Neural Networks [51.589769497681175]
The novel coronavirus (SARS-CoV-2) has led to a pandemic.
The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands.
We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
arXiv Detail & Related papers (2020-07-20T21:47:28Z) - AI outperformed every dermatologist: Improved dermoscopic melanoma
diagnosis through customizing batch logic and loss function in an optimized
Deep CNN architecture [2.572959153453185]
This study proposes a method using deep convolutional neural networks aiming to detect melanoma as a binary classification problem.
It involves 3 key features, namely customized batch logic, customized loss function and reformed fully connected layers.
The model outperformed all 157 dermatologists and achieved state-of-the-art performance with AUC at 94.4% with sensitivity of 85.0% and specificity of 95.0%.
arXiv Detail & Related papers (2020-03-05T13:19:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.