Precise implant placement in the anterior and posterior maxilla often presents challenges due to variable bone and soft tissue anatomy. Many clinicians elect a freehand surgical approach because conventional surgical guides may not always be easy to design, fabricate, or utilize. Guided surgery has been proven to have advantages over freehand surgical protocols and therefore, the present study proposed utilizing the nasopalatine canal (NPC) as an anatomical reference and point of fixation for a novel rotational path surgical template during computer-aided implant surgery (CAIS). The present digital workflow combined artificial intelligence (AI) facilitated cone beam computed tomography (CBCT) software bone segmentation of the maxillary arch to assess the NPC and surrounding hard tissues, to design and fabricate static surgical guides to precisely place implants. After rotational engagement of the maxillary buccal undercuts, each novel surgical guide incorporated the NPC for fixation with a single pin to achieve initial stability. 22 consecutive patients requiring maxillary reconstruction received 123 implants (7 fully and 15 partially edentulous) utilizing a fully-guided surgical protocol to complete 4 overdenture and 18 full-arch fixed restorations. 12 patients required extensive maxillary bone augmentation before implant placement. 13 patients required delayed loading based on bone density and 9 patients were restoratively loaded within 24 to 96 hours post-surgery, accomplished with the use of photogrammetry for the fabrication of 3D-printed restorations. The initial implant success rate was 98.37% and 100% initial prosthetic success. The use of the NPC for fixation of surgical guides did not result in any neurovascular post-operative complications. The novel template concept can improve surgical outcomes using a bone-borne template design for implant-supported rehabilitation of the partial and fully edentulous maxillary arch. Preliminary case series confirmed controlled placement accuracy with limited risk of neurovascular complications for full-arch overdenture and fixed restorations. NPC is a vital maxillary anatomic landmark for implant planning, with an expanded role for the stabilization of novel surgical guide designs due to advancements in AI bone segmentation.

Resting-state functional MRI (rs-fMRI), a promising method for interrogating different brain functional networks from a single MRI acquisition, is increasingly used in clinical presurgical and other pretherapeutic brain mapping. However, challenges in standardization of acquisition, preprocessing, and analysis methods across centers and variability in results interpretation complicate its clinical use. Additionally, inherent problems regarding reliability of language lateralization, interpatient variability of cognitive network representation, dynamic aspects of intranetwork and internetwork connectivity, and effects of neurovascular uncoupling on network detection still must be overcome. Although deep learning solutions and further methodologic standardization will help address these issues, rs-fMRI remains generally considered an adjunct to task-based fMRI (tb-fMRI) for clinical presurgical mapping. Nonetheless, in many clinical instances, rs-fMRI may offer valuable additional information that supplements tb-fMRI, especially if tb-fMRI is inadequate due to patient performance or other limitations. Future growth in clinical applications of rs-fMRI is anticipated as challenges are increasingly addressed. This <i>AJR</i> Expert Panel Narrative Review summarizes the current state and emerging clinical utility of rs-fMRI, focusing on its role in presurgical mapping. Ongoing controversies and limitations in clinical applicability are presented and future directions are discussed, including the developing role of rs-fMRI in neuromodulation treatment of various neurologic disorders.

<b>Background:</b> By reliably classifying screening mammograms as negative, artificial intelligence (AI) could minimize radiologists' time spent reviewing high volumes of normal examinations and help prioritize examinations with high likelihood of malignancy. <b>Objective:</b> To compare performance of AI, classified as positive at different thresholds, with that of radiologists, focusing on NPV and recall rates, in large population-based digital mammography (DM) and digital breast tomosynthesis (DBT) screening cohorts. <b>Methods:</b> This retrospective single-institution study included women enrolled in the observational population-based Athena Breast Health Network. Stratified random sampling was used to identify cohorts of DM and DBT screening examinations performed from January 2010 through December 2019. Radiologists' interpretations were extracted from clinical reports. A commercial AI system classified examinations as low, intermediate, or elevated risk. Breast cancer diagnoses within 1 year after screening examinations were identified from a state cancer registry. AI and radiologist performance were compared. <b>Results:</b> The DM cohort included 26,693 examinations in 20,409 women (mean age, 58.1 years). AI classified 58.2%, 27.7%, and 14.0% of examinations as low, intermediate, and elevated risk, respectively. Sensitivity, specificity, recall rate and NPV for radiologists were 88.6%, 93.3%, 7.2%, and 99.9%; for AI (defining positive as elevated risk), 74.4%, 86.3%, 14.0%, and 99.8%; and for AI (defining positive as intermediate/elevated risk), 94.0%, 58.6%, 41.8%, and 99.9%. The DBT cohort included 4824 examinations in 4379 women (mean age, 61.3 years). AI classified 68.1%, 19.8%, and 12.1% of examinations as low, intermediate, and elevated risk, respectively. Sensitivity, specificity, recall rate, and NPV for radiologists were 83.8%, 93.7%, 6.9%, and 99.9%; for AI (defining positive results as elevated risk), 78.4%, 88.4%, 12.1%, and 99.8%; and for AI (defining positive results as intermediate/elevated risk), 89.2%, 68.5%, 31.9%, and 99.8%. <b>Conclusion:</b> In large DM and DBT cohorts, AI at either diagnostic threshold achieved high NPV but had higher recall rates than radiologists. Defining positive AI results to include intermediate-risk examinations, versus only elevated-risk examinations, detected additional cancers but yielded markedly increased recall rates. <b>Clinical Impact:</b> The findings support AI's potential to aid radiologists' workflow efficiency. Yet, strategies are needed to address frequent false-positive results, particularly in the intermediate-risk category.

2D T2 FSE is an essential routine spine MRI sequence, allowing assessment of fractures, soft tissues, and pathology. Fat suppression using a DIXON-type approach (2D FLEX) improves water/fat separation. Recently, a deep learning (DL) reconstruction (AIR™ Recon DL, GE HealthCare) became available for 2D FLEX, offering increased signal-to-noise ratio (SNR), reduced artifacts, and sharper images. This study aimed to compare DL-reconstructed versus non-DL-reconstructed spine 2D T2 FLEX images for diagnostic image quality and quantitative metrics at 1.5T. Forty-one patients with clinically indicated cervical or lumbar spine MRI were scanned between May and August 2023 on a 1.5T Voyager (GE HealthCare). A 2D T2 FLEX sequence was acquired, and DL-based reconstruction (noise reduction strength: 75%) was applied. Raw data were also reconstructed without DL. Three readers (CAQ-neuroradiologist, PGY-6 neuroradiology fellow, PGY-2 radiology resident) rated diagnostic preference (0 = non-DL, 1 = DL, 2 = equivalent) for 39 cases. Quantitative measures (SNR, total variation [TV], number of edges, and fat fraction [FF]) were compared using paired t-tests with significance set at p < .05. Among evaluations, 79.5% preferred DL, 11% found images equivalent, and 9.4% favored non-DL, with strong inter-rater agreement (p < .001, Fleiss' Kappa = 0.99). DL images had higher SNR, lower TV, and fewer edges (p < .001), indicating effective noise reduction. FF remained statistically unchanged in subcutaneous fat (p = .25) but differed slightly in vertebral bodies (1.4% difference, p = .01). DL reconstruction notably improved image quality by enhancing SNR and reducing noise without clinically meaningful changes in fat quantification. These findings support the use of DL-enhanced 2D T2 FLEX in routine spine imaging at 1.5T. Incorporating DL-based reconstruction into standard spine MRI protocols can increase diagnostic confidence and workflow efficiency. Further studies with larger cohorts and diverse pathologies are warranted to refine this approach and explore potential benefits for clinical decision-making.

Colorectal cancer (CRC) ranks as the second deadliest cancer globally, impacting patients' quality of life. Colonoscopy is the primary screening method for detecting adenomas and polyps, crucial for reducing long-term CRC risk, but it misses about 30% of cases. Efforts to improve detection rates include using AI to enhance colonoscopy. This study assesses the effectiveness and accuracy of a real-time AI-assisted polyp detection system during colonoscopy. The study included 390 patients aged 40 to 75 undergoing colonoscopies for either colorectal cancer screening (risk score ≥ 4) or clinical diagnosis. Participants were randomly assigned to an experimental group using software-assisted diagnosis or a control group with physician diagnosis. The software, a medical image processing tool with B/S and MVC architecture, operates on Windows 10 (64-bit) and supports real-time image handling and lesion identification via HDMI, SDI, AV, and DVI outputs from endoscopy devices. Expert evaluations of retrospective video lesions served as the gold standard. Efficacy was assessed by polyp per colonoscopy (PPC), adenoma per colonoscopy (APC), adenoma detection rate (ADR), and polyp detection rate (PDR), while accuracy was measured using sensitivity and specificity against the gold standard. In this multicenter, randomized controlled trial, computer-aided detection (CADe) significantly improved polyp detection rates (PDR), achieving 67.18% in the CADe group versus 56.92% in the control group. The CADe group identified more polyps, especially those 5 mm or smaller (61.03% vs. 56.92%). In addition, the CADe group demonstrated higher specificity (98.44%) and sensitivity (95.19%) in the FAS dataset, and improved sensitivity (95.82% vs. 77.53%) in the PPS dataset, with both groups maintaining 100% specificity. These results suggest that the AI-assisted system enhances PDR accuracy. This real-time computer-aided polyp detection system enhances efficacy by boosting adenoma and polyp detection rates, while also achieving high accuracy with excellent sensitivity and specificity.

Transplantation of fresh osteochondral allografts is a possible biological resurfacing option to substitute massive bone loss and provide proper gliding surfaces for extended and deep osteochondral lesions of weight-bearing articular surfaces. Limited chondrocyte survival and technical difficulties may compromise the efficacy of osteochondral transfers. As experimental data suggest that minimizing the time between graft harvest and implantation may improve chondrocyte survival rate a <48 hours donor to recipient time was used to repair massive osteochondral defects. For optimal graft congruency, a magnetic resonance-based artificial intelligence algorithm was also developed to provide proper technical support. Based on 3 years of experience, increased survival rate of transplanted chondrocytes and improved clinical outcomes were observed.

Microscopic tumor cell infiltration beyond contrast-enhancing regions influences glioblastoma prognosis but remains undetectable using conventional MRI. To develop and evaluate the glioblastoma infiltrating area interactive detection framework (GIAIDF), an interactive deep-learning framework that integrates diffusion tensor imaging (DTI) biomarkers for identifying microscopic infiltration within peritumoral edema. Retrospective. A total of 73 training patients (51.13 ± 13.87 years; 47 M/26F) and 25 internal validation patients (52.82 ± 10.76 years; 14 M/11F) from Center 1; 25 external validation patients (47.29 ± 11.39 years; 16 M/9F) from Center 2; 13 prospective biopsy patients (45.62 ± 9.28 years; 8 M/5F) from Center 1. 3.0 T MRI including three-dimensional contrast-enhanced T1-weighted BRAVO sequence (repetition time = 7.8 ms, echo time = 3.0 ms, inversion time = 450 ms, slice thickness = 1 mm), three-dimensional T2-weighted fluid-attenuated inversion recovery (repetition time = 7000 ms, echo time = 120 ms, inversion time = 2000 ms, slice thickness = 1 mm), and diffusion tensor imaging (repetition time = 8500 ms, echo time = 63 ms, slice thickness = 2 mm). Histopathology of 25 stereotactic biopsy specimens served as the reference standard. Primary metrics included AUC, accuracy, sensitivity, and specificity. GIAIDF heatmaps were co-registered to biopsy trajectories using Ratio-FAcpcic (0.16-0.22) as interactive priors. ROC analysis (DeLong's method) for AUC; recall, precision, and F1 score for prediction validation. GIAIDF demonstrated recall = 0.800 ± 0.060, precision = 0.915 ± 0.057, F1 = 0.852 ± 0.044 in internal validation (n = 25) and recall = 0.778 ± 0.053, precision = 0.890 ± 0.051, F1 = 0.829 ± 0.040 in external validation (n = 25). Among 13 patients undergoing stereotactic biopsy, 25 peri-ED specimens were analyzed: 18 without tumor cell infiltration and seven with infiltration, achieving AUC = 0.929 (95% CI: 0.804-1.000), sensitivity = 0.714, specificity = 0.944, and accuracy = 0.880. Infiltrated sites showed significantly higher risk scores (0.549 ± 0.194 vs. 0.205 ± 0.175 in non-infiltrated sites, p < 0.001). This study has provided a potential tool, GIAIDF, to identify regions of GBM infiltration within areas of peri-ED based on preoperative MR images.

Advanced MRI techniques, motion-correction and T2*-relaxometry, may provide information regarding functional properties of pulmonary tissue. We assessed whether lung volumes and pulmonary T2* values in fetuses with congenital diaphragmatic hernia (CDH) were lower than controls and differed between survivors and non-survivors. Women with uncomplicated pregnancies (controls) and those with a CDH had a fetal MRI on a 1.5 T imaging system encompassing T2 single shot fast spin echo sequences and gradient echo single shot echo planar sequences providing T2* data. Motion-correction was performed using slice-to-volume reconstruction, T2* maps were generated using in-house pipelines. Lungs were segmented separately using a pre-trained 3D-deep-learning pipeline. Datasets from 33 controls and 12 CDH fetuses were analysed. The mean ± SD gestation at scan was 28.3 ± 4.3 for controls and 27.6 ± 4.9 weeks for CDH cases. CDH lung volumes were lower than controls in both non-survivors and survivors for both lungs combined (5.76 ± 3.59 [cc], mean difference = 15.97, 95% CI: -24.51--12.9, p < 0.001 and 5.73 ± 2.96 [cc], mean difference = 16, 95% CI: 1.91-11.53, p = 0.008) and for the ipsilateral lung (1.93 ± 2.09 [cc], mean difference = 19.8, 95% CI: -28.48--16.45, p < 0.001 1.58 ± 1.18 [cc], mean difference=20.15, 95% CI: 5.96-15.97, p < 0.001). Mean pulmonary T2* values were lower in non-survivors in both lungs, the ipsilateral and contralateral lungs compared with the control group (81.83 ± 26.21 ms, mean difference = 31.13, 95% CI: -58.14--10.32, p = 0.006; 81.05 ± 26.84 ms, mean difference = 31.91, 95% CI: -59.02--10.82, p = 0.006; 82.62 ± 36.31 ms, mean difference = 30.34, 95% CI: -58.84--8.25, p = 0.011) but no difference was observed between controls and CDH cases that survived. Mean pulmonary T2* values were lower in CDH fetuses compared to controls and CDH cases who died compared to survivors. Mean pulmonary T2* values may have a prognostic function in CDH fetuses. This study provides original motion-corrected assessment of the morphologic and functional properties of the ipsilateral and contralateral fetal lungs in the context of CDH. Mean pulmonary T2* values were lower in CDH fetuses compared to controls and in cases who died compared to survivors. Mean pulmonary T2* values may have a role in prognostication. Reduction in pulmonary T2* values in CDH fetuses suggests altered pulmonary development, contributing new insights into antenatal assessment.

To evaluate a deep-learning algorithm for automated coronary artery analysis on ultrahigh-resolution photon-counting detector coronary computed tomography (CT) angiography and compared its performance to expert readers using invasive coronary angiography as reference. Thirty-two patients (mean age 68.6 years; 81 ​% male) underwent both energy-integrating detector and ultrahigh-resolution photon-counting detector CT within 30 days. Expert readers scored each image using the Coronary Artery Disease-Reporting and Data System classification, and compared to invasive angiography. After a three-month wash-out, one reader reanalyzed the photon-counting detector CT images assisted by the algorithm. Sensitivity, specificity, accuracy, inter-reader agreement, and reading times were recorded for each method. On 401 arterial segments, inter-reader agreement improved from substantial (κ ​= ​0.75) on energy-integrating detector CT to near-perfect (κ ​= ​0.86) on photon-counting detector CT. The algorithm alone achieved 85 ​% sensitivity, 91 ​% specificity, and 90 ​% accuracy on energy-integrating detector CT, and 85 ​%, 96 ​%, and 95 ​% on photon-counting detector CT. Compared to invasive angiography on photon-counting detector CT, manual and automated reads had similar sensitivity (67 ​%), but manual assessment slightly outperformed regarding specificity (85 ​% vs. 79 ​%) and accuracy (84 ​% vs. 78 ​%). When the reader was assisted by the algorithm, specificity rose to 97 ​% (p ​< ​0.001), accuracy to 95 ​%, and reading time decreased by 54 ​% (p ​< ​0.001). This deep-learning algorithm demonstrates high agreement with experts and improved diagnostic performance on photon-counting detector CT. Expert review augmented by the algorithm further increases specificity and dramatically reduces interpretation time.

This study explored the feasibility of preoperatively predicting perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) through machine learning based on clinical and CT image features, which may help in individualized clinical decision making and modification of further treatment strategies. This study enrolled 199 patients with histologically confirmed ICC from three institutions for final analysis. 111 patients from Institution I were recruited as the training cohort and internal validation cohort. Significant clinical and CT image features for predicting PNI were screened using the least absolute shrinkage and selection operator (LASSO) to construct machine learning models. 72 patients from Institutions II and III were recruited as two external validation cohorts, and 16 patients from Institution I were enrolled as a prospective cohort to assess model performance. Tumor location (perihilar), intrahepatic bile duct dilatation, and arterial enhancement pattern were selected using LASSO for model construction. Machine learning models were developed based on these three features using five algorithms: multilayer perceptron, random forest, support vector machine, logistic regression, and XGBoost. The AUCs of the models exceeded 0.86, 0.84, 0.79, and 0.72 in the training cohort, internal validation cohort, external validation cohorts, and prospective cohort, respectively. Machine learning models based on CT were accurate in predicting PNI of ICC, which may help in treatment decision making.