Prognostic value of CT findings for conservative treatment failure in adhesive small bowel obstruction.

PURPOSE: This study aimed to identify the radiological CT findings that are significantly correlated with the outcome of conservative management with oral water-soluble contrast medium in patients presenting with Adhesive Small Bowel Obstruction (ASBO) to the Emergency Room. METHODS: In this retrospective single-center study, we considered all consecutive patients admitted to the ER from February 2019 to February 2023 for ASBO with an available contrast-enhanced CT scan performed at diagnosis and treated with conservative management. The investigated CT findings were type and location of transition zone, ASBO degree, fat notch sign, beak sign, small bowel feces sign, presence of peritoneal free fluid and pneumatosis intestinalis. Radiological parameters were analyzed using univariable and multivariable logistic regression to test the significant association between the CT parameters and the target. RESULTS: Among the 106 included patients (median age 74.5 years), conservative treatment was effective in 59 (55.7%) and failed in 47 (44.3%), needing delayed surgery. In the failure group, there was a higher prevalence of patients who had previous ASBO episodes (p = 0.03), a greater proportion of females (p = 0.04) and a longer hospital stay (p < 0.001). At multivariable analysis, two CT findings were significantly correlated with failure of conservative treatment: fat notch sign (OR = 2.95; p = 0.04) and beak sign (OR = 3.42; p = 0.04). CONCLUSIONS: Two radiological signs correlate with failure of non-operative management in ASBO, suggesting their importance in surgical decision-making. Patients presenting with these signs are at higher risk of unsuccessful conservative treatment and may require undelayed surgical intervention.

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence.

Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1-2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.

Sudden neck swelling with rash as late manifestation of COVID-19: a case report.

BACKGROUND: Although there are reports of otolaryngological symptoms and manifestations of CoronaVirus Disease 19 (COVID-19), there have been no documented cases of sudden neck swelling with rash in patients with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection described in literature. CASE PRESENTATION: We report a case of a sudden neck swelling and rash likely due to late SARS-CoV-2 in a 64-year-old woman. The patient reported COVID-19 symptoms over the previous three weeks. Computed Tomography (CT) revealed a diffuse soft-tissue swelling and edema of subcutaneous tissue, hypodermis, and muscular and deep fascial planes. All the differential diagnoses were ruled out. Both the anamnestic history of the patient's husband who had died of COVID-19 with and the collateral findings of pneumonia and esophageal wall edema suggested the association with COVID-19. This was confirmed by nasopharyngeal swab polymerase chain reaction. The patient was treated with lopinavir/ritonavir, hydroxychloroquine and piperacillin/tazobactam for 7 days. The neck swelling resolved in less than 24 h, while the erythema was still present up to two days later. The patient was discharged after seven days in good clinical condition and with a negative swab. CONCLUSION: Sudden neck swelling with rash may be a coincidental presentation, but, in the pandemic context, it is most likely a direct or indirect complication of COVID-19.

Lung Ultrasound and Clinical Progression of Acute Bronchiolitis: A Prospective Observational Single-Center Study.

Background and Objectives: Recent literature suggests that lung ultrasound might have a role in the diagnosis and management of bronchiolitis. The aim of the study is to evaluate the relationship between an ultrasound score and the clinical progression of bronchiolitis: need for supplemental oxygen, duration of oxygen therapy and hospital stay. Materials and Methods: This was a prospective observational single-center study, conducted in a pediatric unit during the 2017-2018 epidemic periods. All consecutive patients admitted with clinical signs of acute bronchiolitis, but without the need for supplemental oxygen, underwent a lung ultrasound in the first 24 h of hospital care. The lung involvement was graded based on the ultrasound score. During clinical progression, need for supplemental oxygen, duration of oxygen therapy and duration of hospital stay were recorded. Results: The final analysis included 83 patients, with a mean age of 4.5 ± 4.1 months. The lung ultrasound score in patients that required supplemental oxygen during hospitalization was 4.5 ± 1.7 (range: 2.0-8.0), different from the one of the not supplemented infants (2.5 ± 1.8; range: 0.0-6.0; p < 0.001). Ultrasound score was associated with the need for supplemental oxygen (OR = 2.2; 95% CI = 1.5-3.3; p < 0.0001). Duration of oxygen therapy was not associated with LUS score (p > 0.05). Length of hospital stay (coef. = 0.5; 95% CI = 0.2-0.7; p < 0.0001) correlates with LUS score. Conclusion: Lung ultrasound score correlates with the need of supplemental oxygen and length of hospital stay in infants with acute bronchiolitis.

nnU-Net-based deep-learning for pulmonary embolism: detection, clot volume quantification, and severity correlation in the RSPECT dataset.

OBJECTIVES: CT pulmonary angiography is the gold standard for diagnosing pulmonary embolism, and DL algorithms are being developed to manage the increase in demand. The nnU-Net is a new auto-adaptive DL framework that minimizes manual tuning, making it easier to develop effective algorithms for medical imaging even without specific expertise. This study assesses the performance of a locally developed nnU-Net algorithm on the RSPECT dataset for PE detection, clot volume measurement, and correlation with right ventricle overload. MATERIALS & METHODS: User input was limited to segmentation using 3DSlicer. We worked with the RSPECT dataset and trained an algorithm from 205 PE and 340 negatives. The test dataset comprised 6573 exams. Performance was tested against PE characteristics, such as central, non-central, and RV overload. Blood clot volume (BCV) was extracted from each exam. We employed ROC curves and logistic regression for statistical validation. RESULTS: Negative studies had a median BCV of 1 µL, which increased to 345 µL in PE-positive cases and 7,378 µL in central PEs. Statistical analysis confirmed a significant BCV correlation with PE presence, central PE, and increased RV/LV ratio (p < 0.0001). The model's AUC for PE detection was 0.865, with an 83 % accuracy at a 55 µL threshold. Central PE detection AUC was 0.937 with 91 % accuracy at 850 µL. The RV overload AUC stood at 0.848 with 79 % accuracy. CONCLUSION: The nnU-Net algorithm demonstrated accurate PE detection, particularly for central PE. BCV is an accurate metric for automated severity stratification and case prioritization. CLINICAL RELEVANCE STATEMENT: The nnU-Net framework can be utilized to create a dependable DL for detecting PE. It offers a user-friendly approach to those lacking expertise in AI and rapidly extracts the Blood Clot Volume, a metric that can evaluate the PE's severity.

A reference framework for standardization and harmonization of CT radiomics features on cadaveric sample.

Radiomics features (RFs) serve as quantitative metrics to characterize shape, density/intensity, and texture patterns in radiological images. Despite their promise, RFs exhibit reproducibility challenges across acquisition settings, thus limiting implementation into clinical practice. In this investigation, we evaluate the effects of different CT scanners and CT acquisition protocols (KV, mA, field-of-view, and reconstruction kernel settings) on RFs extracted from lumbar vertebrae of a cadaveric trunk. Employing univariate and multivariate Generalized Linear Models (GLM), we evaluated the impact of each acquisition parameter on RFs. Our findings indicate that variations in mA had negligible effects on RFs, while alterations in kV resulted in exponential changes in several RFs, notably First Order (94.4%), GLCM (87.5%), and NGTDM (100%). Moreover, we demonstrated that a tailored GLM model was superior to the ComBat algorithm in harmonizing CT images. GLM achieved R2 > 0.90 in 21 RFs (19.6%), contrasting ComBat's mean R2 above 0.90 in only 1 RF (0.9%). This pioneering study unveils the effects of CT acquisition parameters on bone RFs in cadaveric specimens, highlighting significant variations across parameters and scanner datasets. The proposed GLM model presents a robust solution for mitigating these differences, potentially advancing harmonization efforts in Radiomics-based studies across diverse CT protocols and vendors.

Radiomic Gradient in Peritumoural Tissue of Liver Metastases: A Biomarker for Clinical Practice? Analysing Density, Entropy, and Uniformity Variations with Distance from the Tumour.

The radiomic analysis of the tissue surrounding colorectal liver metastases (CRLM) enhances the prediction accuracy of pathology data and survival. We explored the variation of the textural features in the peritumoural tissue as the distance from CRLM increases. We considered patients with hypodense CRLMs >10 mm and high-quality computed tomography (CT). In the portal phase, we segmented (1) the tumour, (2) a series of concentric rims at a progressively increasing distance from CRLM (from one to ten millimetres), and (3) a cylinder of normal parenchyma (Liver-VOI). Sixty-three CRLMs in 51 patients were analysed. Median peritumoural HU values were similar to Liver-VOI, except for the first millimetre around the CRLM. Entropy progressively decreased (from 3.11 of CRLM to 2.54 of Liver-VOI), while uniformity increased (from 0.135 to 0.199, p < 0.001). At 10 mm from CRLM, entropy was similar to the Liver-VOI in 62% of cases and uniformity in 46%. In small CRLMs (≤30 mm) and responders to chemotherapy, normalisation of entropy and uniformity values occurred in a higher proportion of cases and at a shorter distance. The radiomic analysis of the parenchyma surrounding CRLMs unveiled a wide halo of progressively decreasing entropy and increasing uniformity despite a normal radiological aspect. Underlying pathology data should be investigated.

Mapping the research landscape of HPV-positive oropharyngeal cancer: a bibliometric analysis.

OBJECTIVE: The aim of the study is to evaluate the scientific interest, the collaboration patterns and the emerging trends regarding HPV+ OPSCC diagnosis and treatment. MATERIALS AND METHODS: A cross-sectional bibliometric analysis of articles reporting on HPV+ OPSCC within Scopus database was performed and all documents published up to December 31th, 2022 were eligible for analysis. Outcomes included the exploration of key characteristics (number of manuscripts published per year, growth rate, top productive countries, most highly cited papers, and the most well-represented journals), collaboration parameters (international collaboration ratio and networks, co-occurrence networks), keywords analysis (trend topics, factorial analysis). RESULTS: A total of 5200 documents were found, published from March, 1987 to December, 2022. The number of publications increased annually with an average growth rate of 19.94%, reaching a peak of 680 documents published in 2021. The 10 most cited documents (range 1105-4645) were published from 2000 to 2012. The keywords factorial analysis revealed two main clusters: one on epidemiology, diagnosis, prevention and association with other HPV tumors; the other one about the therapeutic options. According to the frequency of keywords, new items are emerging in the last three years regarding the application of Artifical Intelligence (machine learning and radiomics) and the diagnostic biomarkers (circulating tumor DNA). CONCLUSIONS: This bibliometric analysis highlights the importance of research efforts in prevention, diagnostics, and treatment strategies for this disease. Given the urgency of optimizing treatment and improving clinical outcomes, further clinical trials are needed to bridge unaddressed gaps in the management of HPV+ OPSCC patients.

CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae.

Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts.

CT-based radiogenomics of intrahepatic cholangiocarcinoma.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is an aggressive disease with increasing incidence and its genetic alterations could be the target of systemic therapies. AIMS: To elucidate if radiomics extracted from computed tomography (CT) may non-invasively predict ICC genetic alterations. METHODS: All consecutive patients with a diagnosis of a mass-forming ICC (01/2016-06/2022) were considered. Inclusion criteria were availability of a high-quality contrast-enhanced CT and molecular profiling by NGS or FISH for FGFR2 fusion/rearrangement. The CT scan at diagnosis was considered. Genetic analyses were performed on surgical specimens (resectable patients) or biopsies (unresectable ones). The radiomic features were extracted using the LifeX software. Multivariate predictive models of the commonest genetic alterations were built. RESULTS: In the 90 enrolled patients (58 NGS/32 FISH, median age 65 years), the most common genetic alterations were FGFR2 (20/90), IDH1 (10/58), and KRAS (9/58). At internal validation, the combined clinical-radiomic models achieved the best performance for the prediction of FGFR2 (AUC = 0.892) and IDH1 status (AUC = 0.819), outperforming the pure clinical and radiomic models. The radiomic model for predicting KRAS mutations achieved an AUC = 0.767 (vs. 0.660 of the clinical model) without further improvements with the addition of clinical features. CONCLUSIONS: CT-based radiomics provides a reliable non-invasive prediction of ICC genetic status with a major impact on therapeutic strategies.

The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey.

Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals' demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI's potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the 'loss of control' (27.6%) and 'diagnostic errors' (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits.

Advanced Imaging Supports the Mechanistic Role of Autoimmunity and Plaque Rupture in COVID-19 Heart Involvement.

The cardiovascular system is frequently affected by coronavirus disease-19 (COVID-19), particularly in hospitalized cases, and these manifestations are associated with a worse prognosis. Most commonly, heart involvement is represented by myocarditis, myocardial infarction, and pulmonary embolism, while arrhythmias, heart valve damage, and pericarditis are less frequent. While the clinical suspicion is necessary for a prompt disease recognition, imaging allows the early detection of cardiovascular complications in patients with COVID-19. The combination of cardiothoracic approaches has been proposed for advanced imaging techniques, i.e., CT scan and MRI, for a simultaneous evaluation of cardiovascular structures, pulmonary arteries, and lung parenchyma. Several mechanisms have been proposed to explain the cardiovascular injury, and among these, it is established that the host immune system is responsible for the aberrant response characterizing severe COVID-19 and inducing organ-specific injury. We illustrate novel evidence to support the hypothesis that molecular mimicry may be the immunological mechanism for myocarditis in COVID-19. The present article provides a comprehensive review of the available evidence of the immune mechanisms of the COVID-19 cardiovascular injury and the imaging tools to be used in the diagnostic workup. As some of these techniques cannot be implemented for general screening of all cases, we critically discuss the need to maximize the sustainability and the specificity of the proposed tests while illustrating the findings of some paradigmatic cases.

Radiomics-based machine learning for the diagnosis of lymph node metastases in patients with head and neck cancer: Systematic review.

Machine learning (ML) is increasingly used to detect lymph node (LN) metastases in head and neck (H&N) carcinoma. We systematically reviewed the literature on radiomic-based ML for the detection of pathological LNs in H&N cancer. A systematic review was conducted in PubMed, EMBASE, and the Cochrane Library. Baseline study characteristics and methodological quality items (modeling, performance evaluation, clinical utility, and transparency items) were extracted and evaluated. The qualitative synthesis is presented using descriptive statistics. Seven studies were included in this study. Overall, the methodological quality items were generally favorable for modeling (57% of studies). The studies were mostly unsuccessful in terms of transparency (85.7%), evaluation of clinical utility (71.3%), and assessment of generalizability employing independent or external validation (72.5%). ML may be able to predict LN metastases in H&N cancer. Further studies are warranted to improve the generalizability assessment, clinical utility evaluation, and transparency items.

Image Embeddings Extracted from CNNs Outperform Other Transfer Learning Approaches in Classification of Chest Radiographs.

To identify the best transfer learning approach for the identification of the most frequent abnormalities on chest radiographs (CXRs), we used embeddings extracted from pretrained convolutional neural networks (CNNs). An explainable AI (XAI) model was applied to interpret black-box model predictions and assess its performance. Seven CNNs were trained on CheXpert. Three transfer learning approaches were thereafter applied to a local dataset. The classification results were ensembled using simple and entropy-weighted averaging. We applied Grad-CAM (an XAI model) to produce a saliency map. Grad-CAM maps were compared to manually extracted regions of interest, and the training time was recorded. The best transfer learning model was that which used image embeddings and random forest with simple averaging, with an average AUC of 0.856. Grad-CAM maps showed that the models focused on specific features of each CXR. CNNs pretrained on a large public dataset of medical images can be exploited as feature extractors for tasks of interest. The extracted image embeddings contain relevant information that can be used to train an additional classifier with satisfactory performance on an independent dataset, demonstrating it to be the optimal transfer learning strategy and overcoming the need for large private datasets, extensive computational resources, and long training times.

Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review.

The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies.

Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study.

(1) Background: Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between residual respiratory symptoms and abnormal QCT results. (2) Methods: In this prospective monocentric trial performed during the "first wave" of the Italian pandemic, i.e., from March to May 2020, we aimed to test the relationship between %deltaCL (variation of %CL-compromised lung volume) and variations of symptoms-dyspnea, cough and chest pain-at follow-up clinical assessment after hospitalization. (3) Results: 282 patients (95 females, 34%) with a median age of 60 years (IQR, 51-69) were included. We reported a correlation between changing lung abnormalities measured by QCT, and residual symptoms at short-term follow up after COVID-19 pneumonia. Independently from age, a low percentage of surviving patients (1-4%) may present residual respiratory symptoms at approximately two months after discharge. QCT was able to quantify the extent of residual lung damage underlying such symptoms, as the reduction of both %PAL (poorly aerated lung) and %CL volumes was correlated to their disappearance. (4) Conclusions QCT may be used as an objective metric for the measurement of COVID-19 sequelae.

The added value of artificial intelligence to LI-RADS categorization: A systematic review.

PURPOSE: The objective of this systematic review was to critically assess the available literature on deep learning (DL) and radiomics applied to the Liver Imaging Reporting and Data System (LI-RADS) in terms of 1) automatic LI-RADS classification of liver nodules; 2) the contribution of DL and radiomics to human evaluation in the classification of liver nodules following LI-RADS protocol. MATERIALS AND METHODS: A literature search was conducted to identify original research studies published up to April 2021. The inclusion criteria were: English language, focus on computed tomography (CT) and/or magnetic resonance (MR) with specified number of patients and lesions, adoption of LI-RADS classification for the detected hepatic lesions, and application of AI in the classification of liver nodules. Review articles, conference papers, editorials and commentaries, animal studies or studies with absence of AI and/or LI-RADS were excluded. After screening 221 articles, 11 studies were included in this review. RESULTS: All the included studies proved that DL and radiomics have high performances in liver nodules classification, sometimes similar or better than human evaluation. The best performances of DL was an AUC of 0.95 on MR and the best performance of radiomics was AUC of 0.98 either on CT and MR, while the lower ones were respectively AUC of 0.63 either on CT and MR for DL and AUC of 0.70 on CT for radiomics. CONCLUSION: DL and radiomics could be a useful tool in assisting radiologists in the diagnosis and classification of liver nodules according to LI-RADS.

Lung Ultrasound in Pediatrics and Neonatology: An Update.

The potential role of ultrasound for the diagnosis of pulmonary diseases is a recent field of research, because, traditionally, lungs have been considered unsuitable for ultrasonography for the high presence of air and thoracic cage that prevent a clear evaluation of the organ. The peculiar anatomy of the pediatric chest favors the use of lung ultrasound (LUS) for the diagnosis of respiratory conditions through the interpretation of artefacts generated at the pleural surface, correlating them to disease-specific patterns. Recent studies demonstrate that LUS can be a valid alternative to chest X-rays for the diagnosis of pulmonary diseases, especially in children to avoid excessive exposure to ionizing radiations. This review focuses on the description of normal and abnormal findings during LUS of the most common pediatric pathologies. Current literature demonstrates usefulness of LUS that may become a fundamental tool for the whole spectrum of lung pathologies to guide both diagnostic and therapeutic decisions.

Percutaneous Treatment of Bronchobiliary Fistula: Report of a Successful Transhepatic Embolization and a Decision-Making Strategy Driven by Systematic Literature Review.

Bronchobiliary fistula is a rare condition characterized by bile leaking into the bronchial tree causing biliptysis. It may arise from liver infection or as a consequence of resection and thermal ablation of cancer. Currently, there is no consensus about the treatment strategy. Surgery is considered the main therapy by most authors. However, this systematic literature review shows that the success rate of percutaneous treatments may reach 75%. Adding to such evidence, we also report the case of a woman affected by iatrogenic bronchobiliary fistula secondary to liver thermal ablation, successfully treated with percutaneous drainage plus embolization. Summarizing these results, we encourage the percutaneous management of bronchobiliary fistula by providing a 3-step decision-making algorithm, aimed at reducing the need for major surgery.

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review.

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.