Artificial intelligence for the detection of airway nodules in chest CT scans.

Objectives: Incidental airway tumors are rare and can easily be overlooked on chest CT, especially at an early stage. Therefore, we developed and assessed a deep learning-based artificial intelligence (AI) system for detecting and localizing airway nodules.

Materials And Methods: At a single academic hospital, we retrospectively analyzed cancer diagnoses and radiology reports from patients who received a chest or chest-abdomen CT scan between 2004 and 2020 to find cases presenting as airway nodules.

Segmenting the Inferior Alveolar Canal in CBCTs Volumes: the ToothFairy Challenge.

In recent years, several algorithms have been developed for the segmentation of the Inferior Alveolar Canal (IAC) in Cone-Beam Computed Tomography (CBCT) scans. However, the availability of public datasets in this domain is limited, resulting in a lack of comparative evaluation studies on a common benchmark. To address this scientific gap and encourage deep learning research in the field, the ToothFairy challenge was organized within the MICCAI 2023 conference.

Machine-learning based prediction of appendicitis for patients presenting with acute abdominal pain at the emergency department.

Background: Acute abdominal pain (AAP) constitutes 5-10% of all emergency department (ED) visits, with appendicitis being a prevalent AAP etiology often necessitating surgical intervention. The variability in AAP symptoms and causes, combined with the challenge of identifying appendicitis, complicate timely intervention. To estimate the risk of appendicitis, scoring systems such as the Alvarado score have been developed.

Automated tooth segmentation in magnetic resonance scans using deep learning - A pilot study.

Objectives: The main objective was to develop and evaluate an artificial intelligence model for tooth segmentation in magnetic resonance (MR) scans.

Methods: MR scans of 20 patients performed with a commercial 64-channel head coil with a T1-weighted 3D-SPACE (Sampling Perfection with Application Optimized Contrasts using different flip angle Evolution) sequence were included. Sixteen datasets were used for model training and 4 for accuracy evaluation.

AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images.

Objectives: The assessment of lumbar central canal stenosis (LCCS) is crucial for diagnosing and planning treatment for patients with low back pain and neurogenic pain. However, manual assessment methods are time-consuming, variable, and require axial MRIs. The aim of this study is to develop and validate an AI-based model that automatically classifies LCCS using sagittal T2-weighted MRIs.

Evaluation of C-Reactive Protein and Computer-Aided Analysis of Chest X-rays as Tuberculosis Triage Tests at Health Facilities in Lesotho and South Africa.

Background: To improve tuberculosis case-finding, rapid, non-sputum triage tests need to be developed according to the World Health Organization target product profile (TPP) (>90% sensitivity, >70% specificity). We prospectively evaluated and compared artificial intelligence-based, computer-aided detection software, CAD4TBv7, and C-reactive protein assay (CRP) as triage tests at health facilities in Lesotho and South Africa.

Methods: Adults (≥18 years) presenting with ≥1 of the 4 cardinal tuberculosis symptoms were consecutively recruited between February 2021 and April 2022.

Structure and position-aware graph neural network for airway labeling.

We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation. The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are extracted using convolutional neural networks and enriched using graph neural networks. Our graph neural network is structure-aware by having each node aggregate information from its local neighbors and position-aware by encoding node positions in the graph.

Uncertainty-aware multiple-instance learning for reliable classification: Application to optical coherence tomography.

Deep learning classification models for medical image analysis often perform well on data from scanners that were used to acquire the training data. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability.

Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data.

Background: Hemoglobinopathies, the most common inherited blood disorder, are frequently underdiagnosed. Early identification of carriers is important for genetic counseling of couples at risk. The aim of this study was to develop and validate a novel machine learning model on a multicenter data set, covering a wide spectrum of hemoglobinopathies based on routine complete blood count (CBC) testing.

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study.

Background: Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.

The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data.

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent.

Integrating molecular and radiological screening tools during community-based active case-finding for tuberculosis and COVID-19 in southern Africa.

Objectives: To evaluate diagnostic yield and feasibility of integrating testing for TB and COVID-19 using molecular and radiological screening tools during community-based active case-finding (ACF).

Methods: Community-based participants with presumed TB and/or COVID-19 were recruited using a mobile clinic. Participants underwent simultaneous point-of-care (POC) testing for TB (sputum; Xpert Ultra) and COVID-19 (nasopharyngeal swabs; Xpert SARS-CoV-2).

Artificial intelligence for automated detection and measurements of carpal instability signs on conventional radiographs.

Objectives: To develop and validate an artificial intelligence (AI) system for measuring and detecting signs of carpal instability on conventional radiographs.

Materials And Methods: Two case-control datasets of hand and wrist radiographs were retrospectively acquired at three hospitals (hospitals A, B, and C). Dataset 1 (2178 radiographs from 1993 patients, hospitals A and B, 2018-2019) was used for developing an AI system for measuring scapholunate (SL) joint distances, SL and capitolunate (CL) angles, and carpal arc interruptions.

Combining public datasets for automated tooth assessment in panoramic radiographs.

Objective: Panoramic radiographs (PRs) provide a comprehensive view of the oral and maxillofacial region and are used routinely to assess dental and osseous pathologies. Artificial intelligence (AI) can be used to improve the diagnostic accuracy of PRs compared to bitewings and periapical radiographs. This study aimed to evaluate the advantages and challenges of using publicly available datasets in dental AI research, focusing on solving the novel task of predicting tooth segmentations, FDI numbers, and tooth diagnoses, simultaneously.

Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images.

Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers have presented systems for the automatic quantification of TPS, but none report on the task of determining cell-level PD-L1 expression and often reserve their evaluation to a single PD-L1 monoclonal antibody or clinical center. In this paper, we report on a deep learning algorithm for detecting PD-L1 negative and positive tumor cells at a cellular level and evaluate it on a cell-level reference standard established by six readers on a multi-centric, multi PD-L1 assay dataset.

Nodule Detection and Generation on Chest X-Rays: NODE21 Challenge.

Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task.

Lumbar spine segmentation in MR images: a dataset and a public benchmark.

This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain and was collected from four different hospitals. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images.

Development and validation of AI-based automatic measurement of coronal Cobb angles in degenerative scoliosis using sagittal lumbar MRI.

Objectives: Severity of degenerative scoliosis (DS) is assessed by measuring the Cobb angle on anteroposterior radiographs. However, MRI images are often available to study the degenerative spine. This retrospective study aims to develop and evaluate the reliability of a novel automatic method that measures coronal Cobb angles on lumbar MRI in DS patients.

Metrics reloaded: recommendations for image analysis validation.

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers.