AI in Lung Health: Benchmarking Detection and Diagnostic Models Across Multiple CT Scan Datasets

• URL: http://arxiv.org/abs/2405.04605v2
• Date: Wed, 12 Jun 2024 22:18:41 GMT
• Title: AI in Lung Health: Benchmarking Detection and Diagnostic Models Across Multiple CT Scan Datasets
• Authors: Fakrul Islam Tushar, Avivah Wang, Lavsen Dahal, Michael R. Harowicz, Kyle J. Lafata, Tina D. Tailor, Joseph Y. Lo,
• Abstract summary: Lung cancer's high mortality rate can be mitigated by early detection, increasingly reliant on AI for diagnostic imaging. This study develops and validates AI models for both nodule detection and cancer classification tasks.
• Score: 0.33923727961771083
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Lung cancer's high mortality rate can be mitigated by early detection, increasingly reliant on AI for diagnostic imaging. However, AI model performance depends on training and validation datasets. This study develops and validates AI models for both nodule detection and cancer classification tasks. For detection, two models (DLCSD-mD and LUNA16-mD) were developed using the Duke Lung Cancer Screening Dataset (DLCSD), with over 2,000 CT scans from 1,613 patients and more than 3,000 annotations. These models were evaluated on internal (DLCSD) and external datasets, including LUNA16 (601 patients, 1186 nodules) and NLST (969 patients, 1192 nodules), using FROC analysis and AUC metrics. For classification, five models were developed and tested: a randomly initialized 3D ResNet50, Genesis, MedNet3D, an enhanced ResNet50 using Strategic Warm-Start++ (SWS++), and a linear classifier analyzing features from the Foundation Model for Cancer Biomarkers (FMCB). These models were trained to distinguish between benign and malignant nodules and evaluated using AUC analysis on internal (DLCSD) and external datasets, including LUNA16 (433 patients, 677 nodules) and NLST. The DLCSD-mD model achieved an AUC of 0.93 (95% CI: 0.91-0.94) on the internal DLCSD dataset. External validation results were 0.97 (95% CI: 0.96-0.98) on LUNA16 and 0.75 (95% CI: 0.73-0.76) on NLST. For classification, the ResNet50-SWS++ model recorded AUCs of 0.71 (95% CI: 0.61-0.81) on DLCSD, 0.90 (95% CI: 0.87-0.93) on LUNA16, and 0.81 (95% CI: 0.79-0.82) on NLST. Other models showed varying performance across datasets, underscoring the importance of diverse model approaches. This benchmarking establishes DLCSD as a reliable resource for lung cancer AI research.

Related papers

• Multi-centric AI Model for Unruptured Intracranial Aneurysm Detection and Volumetric Segmentation in 3D TOF-MRI [6.397650339311053]
  We developed an open-source nnU-Net-based AI model for combined detection and segmentation of unruptured intracranial aneurysms (UICA) in 3D TOF-MRI. Four distinct training datasets were created, and the nnU-Net framework was used for model development. The primary model showed 85% sensitivity and 0.23 FP/case rate, outperforming the ADAM-challenge winner (61%) and a nnU-Net trained on ADAM data (51%) in sensitivity.
  arXiv  Detail & Related papers  (2024-08-30T08:57:04Z)
• 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)
• Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets [6.712251433139412]
  Medical image foundation models have shown the ability to segment organs and tumors with minimal fine-tuning. These models are typically evaluated on task-specific in-distribution (ID) datasets. We introduced a comprehensive set of computationally fast metrics to evaluate the performance of multiple foundation models trained with self-supervised learning (SSL) SMIT produced a highest F1-score (LRAD: 0.60, 5Rater: 0.64) and lowest entropy (LRAD: 0.06, 5Rater: 0.12), indicating higher tumor detection rate and confident segmentations.
  arXiv  Detail & Related papers  (2024-03-19T19:36:48Z)
• Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification [52.77024349608834]
  To investigate chest radiograph (CXR) classification performance of vision transformers (ViT) and interpretability of attention-based saliency. ViTs were fine-tuned for lung disease classification using four public data sets: CheXpert, Chest X-Ray 14, MIMIC CXR, and VinBigData. ViTs had comparable CXR classification AUCs compared with state-of-the-art CNNs.
  arXiv  Detail & Related papers  (2023-03-03T12:05:41Z)
• A Deep Learning Based Workflow for Detection of Lung Nodules With Chest Radiograph [0.0]
  We built a segmentation model to identify lung areas from CXRs, and sliced them into 16 patches. These labeled patches were then used to train finetune a deep neural network(DNN) model, classifying the patches as positive or negative.
  arXiv  Detail & Related papers  (2021-12-19T16:19:46Z)
• 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)
• Semi-supervised learning for generalizable intracranial hemorrhage detection and segmentation [0.0]
  We develop and evaluate a semisupervised learning model for intracranial hemorrhage detection and segmentation on an outofdistribution head CT evaluation set. An initial "teacher" deep learning model was trained on 457 pixel-labeled head CT scans collected from one US institution from 2010-2017. A second "student" model was trained on this combined pixel-labeled and pseudo-labeled dataset.
  arXiv  Detail & Related papers  (2021-05-03T00:14:43Z)
• 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)
• Chest x-ray automated triage: a semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures [83.48996461770017]
  This work presents a Deep Learning method based on the late fusion of different convolutional architectures. We built four training datasets combining images from public chest x-ray datasets and our institutional archive. We trained four different Deep Learning architectures and combined their outputs with a late fusion strategy, obtaining a unified tool.
  arXiv  Detail & Related papers  (2020-12-23T14:38:35Z)
• 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)
• Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation [9.808639780672156]
  Elderly patients with MODS have high risk of death and poor prognosis. This study aims to develop an interpretable and generalizable model for early mortality prediction in elderly patients with MODS.
  arXiv  Detail & Related papers  (2020-01-28T17:15:34Z)

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.