Dental implant surgery has become a prevalent treatment option for patients with single tooth defects. However, the success of this surgery relies heavily on precise planning and execution. This study investigates the application of artificial intelligence (AI) in assisting the planning process of dental implant surgery for single tooth defects. Single tooth defects in the oral cavity pose a significant challenge in restorative dentistry. Dental implant restoration has emerged as an effective solution for rehabilitating such defects. However, the complexity of the procedure and the need for accurate treatment planning necessitate the integration of advanced technologies. In this study, we propose the utilisation of AI to enhance the precision and efficiency of implant surgery planning for single tooth defects. A total of twenty patients with single tooth loss were enrolled. Cone-beam computed tomography (CBCT) and intra-oral scans were obtained and imported into the AI-dentist software for 3D reconstruction. AI assisted in implant selection, tooth position identification, and crown fabrication. Evaluation included subjective verification and objective assessments. A paired samples t-test was used to compare planning times (dentist vs. AI), with a significance level of p < 0.05. Twenty patients (9 male, 11 female; mean age 59.5 ± 11.86 years) with single missing teeth participated in this study. Implant margins were carefully positioned: 3.05 ± 1.44 mm from adjacent roots, 2.52 ± 0.65 mm from bone plate edges, 3.05 ± 1.44 mm from sinus/canal, and 3.85 ± 1.23 mm from gingival height. Manual planning (21.50 ± 4.87 min) was statistically significantly slower than AI (11.84 ± 3.22 min, p < 0.01). Implant planning met 100% buccolingual/proximal/distal bone volume criteria and 90% sinus/canal distance criteria. Two patients required sinus lifting and bone grafting due to insufficient bone volume. This study highlights the promising role of AI in enhancing the precision and efficiency of dental implant surgery planning for single tooth defects. Further studies are necessary to validate the effectiveness and safety of AI-assisted planning in a larger patient population.

To develop a deep learning (DL) model for predicting disease-free survival (DFS) in clinical stage I lung cancer patients who underwent surgical resection using pre-treatment CT images, and further validate it in patients receiving stereotactic body radiation therapy (SBRT). A retrospective cohort of 2489 clinical stage I non-small cell lung cancer (NSCLC) patients treated with operation (2015-2017) was enrolled to develop a DL-based DFS prediction model. Tumor features were extracted from CT images using a three-dimensional convolutional neural network. External validation was performed on 248 clinical stage I patients receiving SBRT from two hospitals. A clinical model was constructed by multivariable Cox regression for comparison. Model performance was evaluated with Harrell's concordance index (C-index), which measures the model's ability to correctly rank survival times by comparing all possible pairs of subjects. In the surgical cohort, the DL model effectively predicted DFS with a C-index of 0.85 (95% CI: 0.80-0.89) in the internal testing set, significantly outperforming the clinical model (C-index: 0.76). Based on the DL model, 68 patients in the SBRT cohort identified as high-risk had significantly worse DFS compared to the low-risk group (p < 0.01, 5-year DFS rate: 34.7% vs 77.4%). The DL-score was demonstrated to be an independent predictor of DFS in both cohorts (p < 0.01). The CT-based DL model improved DFS prediction in clinical stage I lung cancer patients, identifying populations at high risk of recurrence and metastasis to guide clinical decision-making. Question The recurrence or metastasis rate of early-stage lung cancer remains high and varies among patients following radical treatments such as surgery or SBRT. Findings This CT-based DL model successfully predicted DFS and stratified varying disease risks in clinical stage I lung cancer patients undergoing surgery or SBRT. Clinical relevance The CT-based DL model is a reliable predictive tool for the prognosis of early-stage lung cancer. Its accurate risk stratification assists clinicians in identifying specific patients for personalized clinical decision making.

To investigate the feasibility of accelerated MRI with artificial intelligence-assisted compressed sensing (ACS) technique in the temporomandibular joint (TMJ) and compare its performance with parallel imaging (PI) protocol and standard (STD) protocol. Participants with TMJ-related symptoms were prospectively enrolled from April 2023 to May 2024, and underwent bilateral TMJ imaging examinations using ACS protocol (6:08 min), PI protocol (10:57 min), and STD protocol (13:28 min). Overall image quality and visibility of TMJ relevant structures were qualitatively evaluated by a 4-point Likert scale. Quantitative analysis of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of TMJ disc, condyle, and lateral pterygoid muscle (LPM) was performed. Diagnostic agreement of joint effusion and disc displacement among protocols and investigators was assessed by Fleiss' kappa analysis. A total of 51 participants (16 male and 35 female) with 102 TMJs were included. The overall image quality and most structures of the ACS protocol were significantly higher than the STD protocol (all p < 0.05), and similar to the PI protocol. For quantitative analysis, the ACS protocol demonstrated significantly higher SNR and CNR than the STD protocol in the TMJ disc, condyle, and LPM (all p < 0.05), and the ACS protocol showed comparable SNR to the PI protocol in most sequences. Good to excellent inter-protocol and inter-observer agreement was observed for diagnosing TMJ abnormalities (κ = 0.699-1.000). Accelerated MRI with ACS technique can significantly reduce the acquisition time of TMJ, while providing superior or equivalent image quality and great diagnostic agreement with PI and STD protocols. Question Patients with TMJ disorders often cannot endure long MRI examinations due to orofacial pain, necessitating accelerated MRI to improve patient comfort. Findings ACS technique can significantly reduce acquisition time in TMJ imaging while providing superior or equivalent image quality. Clinical relevance The time-saving ACS technique improves image quality and achieves excellent diagnostic agreement in the evaluation of joint effusion and disc displacement. It helps optimize clinical MRI workflow in patients with TMJ disorders.

Delayed neurological sequelae are among the most serious complications of carbon monoxide poisoning. However, no reliable tools are available for evaluating its potential risk. We aimed to assess whether machine learning models using imaging features that were automatically extracted from brain MRI can predict the potential delayed neurological sequelae risk in patients with acute carbon monoxide poisoning. This single-center, retrospective, observational study analyzed a prospectively collected registry of acute carbon monoxide poisoning patients who visited our emergency department from April 2011 to December 2015. Overall, 1618 radiomics and 4 lesion-segmentation features from DWI b1000 and ADC images, as well as 62 clinical variables were extracted from each patient. The entire dataset was divided into five subsets, with one serving as the hold-out test set and the remaining four used for training and tuning. Four machine learning models, linear regression, support vector machine, random forest, and extreme gradient boosting, as well as an ensemble model, were trained and evaluated using 20 different data configurations. The primary evaluation metric was the mean and 95% CI of the area under the receiver operating characteristic curve. Shapley additive explanations were calculated and visualized to enhance model interpretability. Of the 373 patients, delayed neurological sequelae occurred in 99 (26.5%) patients (mean age 43.0 ± 15.2; 62.0% male). The means [95% CIs] of the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of the best performing machine learning model for predicting the development of delayed neurological sequelae were 0.88 [0.86-0.9], 0.82 [0.8-0.83], 0.81 [0.79-0.83], and 0.82 [0.8-0.84], respectively. Among imaging features, the presence, size, and number of acute brain lesions on DWI b1000 and ADC images more accurately predicted DNS risk than advanced radiomics features based on shape, texture and wavelet transformation. Machine learning models developed using automatically extracted brain MRI features with clinical features can distinguish patients at delayed neurological sequelae risk. The models enable effective prediction of delayed neurological sequelae in patients with acute carbon monoxide poisoning, facilitating timely treatment planning for prevention. ABL = Acute brain lesion; AUROC = area under the receiver operating characteristic curve; CO = carbon monoxide; DNS = delayed neurological sequelae; LR = logistic regression; ML = machine learning; RF = random forest; SVM = support vector machine; XGBoost = extreme gradient boosting.

Prognosis for thoracic aortic aneurysms is significantly worse for women than men, with a higher mortality rate observed among female patients. The increasing use of magnetic resonance breast imaging (MRI) offers a unique opportunity for simultaneous detection of both breast cancer and thoracic aortic aneurysms. We retrospectively validate a fully-automated artificial neural network (ANN) pipeline on 5057 breast MRI examinations from public (Duke University Hospital/EA1141 trial) and in-house (Erlangen University Hospital) data. The ANN, benchmarked against 3D-ground-truth segmentations, clinical reports, and a multireader panel, demonstrates high technical robustness (dice/clDice 0.88-0.91/0.97-0.99) across different vendors and field strengths. The ANN improves aneurysm detection rates by 3.5-fold compared with routine clinical readings, highlighting its potential to improve early diagnosis and patient outcomes. Notably, a higher odds ratio (OR = 2.29, CI: [0.55,9.61]) for thoracic aortic aneurysms is observed in women with breast cancer or breast cancer history, suggesting potential further benefits from integrated simultaneous assessment for cancer and aortic aneurysms.

In medical imaging, challenges are competitions that aim to provide a fair comparison of different methodologic solutions to a common problem. Challenges typically focus on addressing real-world problems, such as segmentation, detection, and prediction tasks, using various types of medical images and associated data. Here, we describe the organization and results of such a challenge to compare machine-learning models for predicting survival in patients with diffuse large B-cell lymphoma using a baseline ¹⁸F-FDG PET/CT radiomics dataset. <b>Methods:</b> This challenge aimed to predict progression-free survival (PFS) in patients with diffuse large B-cell lymphoma, either as a binary outcome (shorter than 2 y versus longer than 2 y) or as a continuous outcome (survival in months). All participants were provided with a radiomic training dataset, including the ground truth survival for designing a predictive model and a radiomic test dataset without ground truth. Figures of merit (FOMs) used to assess model performance were the root-mean-square error for continuous outcomes and the C-index for 1-, 2-, and 3-y PFS binary outcomes. The challenge was endorsed and initiated by the Society of Nuclear Medicine and Molecular Imaging AI Task Force. <b>Results:</b> Nineteen models for predicting PFS as a continuous outcome from 15 teams were received. Among those models, external validation identified 6 models showing similar performance to that of a simple general linear reference model using SUV and total metabolic tumor volumes (TMTV) only. Twelve models for predicting binary outcomes were submitted by 9 teams. External validation showed that 1 model had higher, but nonsignificant, C-index values compared with values obtained by a simple logistic regression model using SUV and TMTV. <b>Conclusion:</b> Some of the radiomic-based machine-learning models developed by participants showed better FOMs than did simple linear or logistic regression models based on SUV and TMTV only, although the differences in observed FOMs were nonsignificant. This suggests that, for the challenge dataset, there was limited or no value seen from the addition of sophisticated radiomic features and use of machine learning when developing models for outcome prediction.

Prolonged imaging times and motion sensitivity at 7T necessitate advancements in image acceleration techniques. This study evaluates a 7T deep learning (DL)-based image reconstruction by using a deep neural network trained on 7T data, applied to T2-weighted turbo spin-echo imaging. Raw <i>k</i>-space data from 30 consecutive clinical 7T brain MRI patients was reconstructed by using both DL and standard methods. Qualitative assessments included overall image quality, artifacts, sharpness, structural conspicuity, and noise level, while quantitative metrics evaluated contrast-to-noise ratio (CNR) and image noise. DL-based reconstruction consistently outperformed standard methods across all qualitative metrics (<i>P</i> < .001), with a mean CNR increase of 50.8% (95% CI: 43.0%-58.6%) and a mean noise reduction of 35.1% (95% CI: 32.7%-37.6%). These findings demonstrate that DL-based reconstruction at 7T significantly enhances image quality without introducing adverse effects, offering a promising tool for addressing the challenges of ultra-high-field MRI.

Robustness against input data perturbations is essential for deploying deep learning models in clinical practice. Adversarial attacks involve subtle, voxel-level manipulations of scans to increase deep learning models' prediction errors. Testing deep learning model performance on examples of adversarial images provides a measure of robustness, and including adversarial images in the training set can improve the model's robustness. In this study, we examined adversarial training and input modifications to improve the robustness of deep learning models in predicting hematoma expansion (HE) from admission head CTs of patients with acute intracerebral hemorrhage (ICH). We used a multicenter cohort of <i>n</i> = 890 patients for cross-validation/training, and a cohort of <i>n</i> = 684 consecutive patients with ICH from 2 stroke centers for independent validation. Fast gradient sign method (FGSM) and projected gradient descent (PGD) adversarial attacks were applied for training and testing. We developed and tested 4 different models to predict ≥3 mL, ≥6 mL, ≥9 mL, and ≥12 mL HE in an independent validation cohort applying receiver operating characteristics area under the curve (AUC). We examined varying mixtures of adversarial and nonperturbed (clean) scans for training as well as including additional input from the hyperparameter-free Otsu multithreshold segmentation for model. When deep learning models trained solely on clean scans were tested with PGD and FGSM adversarial images, the average HE prediction AUC decreased from 0.8 to 0.67 and 0.71, respectively. Overall, the best performing strategy to improve model robustness was training with 5:3 mix of clean and PGD adversarial scans and addition of Otsu multithreshold segmentation to model input, increasing the average AUC to 0.77 against both PGD and FGSM adversarial attacks. Adversarial training with FGSM improved robustness against similar type attack but offered limited cross-attack robustness against PGD-type images. Adversarial training and inclusion of threshold-based segmentation as an additional input can improve deep learning model robustness in prediction of HE from admission head CTs in acute ICH.

The integration of computed tomography-derived fractional flow reserve (CT-FFR), utilizing computational fluid dynamics and artificial intelligence (AI) in routine coronary computed tomographic angiography (CCTA), presents a promising approach to enhance evaluations of functional lesion severity. Extensive evidence underscores the diagnostic accuracy, prognostic significance, and clinical relevance of CT-FFR, prompting recent clinical guidelines to recommend its combined use with CCTA for selected individuals with with intermediate stenosis on CCTA and stable or acute chest pain. This manuscript critically examines the existing clinical evidence, evaluates the diagnostic performance, and outlines future perspectives for integrating noninvasive assessments of coronary anatomy and physiology. Furthermore, it serves as a practical guide for medical imaging professionals by addressing common pitfalls and challenges associated with CT-FFR while proposing potential solutions to facilitate its successful implementation in clinical practice.

Prompt diagnosis of giant cell arteritis (GCA) with ultrasound is crucial for preventing severe ocular and other complications, yet expertise in ultrasound performance is scarce. The development of an artificial intelligence (AI)-based assistant that facilitates ultrasound image classification and helps to diagnose GCA early promises to close the existing gap. In the projection of the planned AI, this study investigates the minimum image resolution required for human experts to reliably classify ultrasound images of arteries commonly affected by GCA for the presence or absence of GCA. Thirty-one international experts in GCA ultrasonography participated in a web-based exercise. They were asked to classify 10 ultrasound images for each of 5 vascular segments as GCA, normal, or not able to classify. The following segments were assessed: (1) superficial common temporal artery, (2) its frontal and (3) parietal branches (all in transverse view), (4) axillary artery in transverse view, and 5) axillary artery in longitudinal view. Identical images were shown at different resolutions, namely 32 × 32, 64 × 64, 128 × 128, 224 × 224, and 512 × 512 pixels, thereby resulting in a total of 250 images to be classified by every study participant. Classification performance improved with increasing resolution up to a threshold, plateauing at 224 × 224 pixels. At 224 × 224 pixels, the overall classification sensitivity was 0.767 (95% CI, 0.737-0.796), and specificity was 0.862 (95% CI, 0.831-0.888). A resolution of 224 × 224 pixels ensures reliable human expert classification and aligns with the input requirements of many common AI-based architectures. Thus, the results of this study substantially guide projected AI development.