Diagnostic Accuracy Research Articles

Comparison of Diagnostic Accuracies of Commonly Used Trial of Void Parameters.

Understanding the diagnostic accuracy of postoperative trial of void (TOV) parameters is important for decision making related to postoperative catheterization. The aim of the study was to compare the diagnostic accuracies of common postoperative TOV parameters. The study population comprised a prospective cohort undergoing outpatient urogynecologic procedures at a tertiary referral center from September 2018 to June 2021. Participants recorded their postvoid residual volume (PVR), voided volume, and subjective force of stream (sFOS) for all postoperative voids until meeting criteria to stop. The primary outcome was the sensitivity of TOV parameters in predicting postoperative urinary retention, defined as PVR ≥1/2 voided volume on the first 2 postoperative voids. Sample size was set at 183 to detect a 20% difference (α = 0.05, β = 0.2, up to 20% with missing data) in sensitivity between TOV parameters. Diagnostic accuracies were compared with McNemar's test for paired proportions, with Youden's index calculated to determine optimal thresholds. The 160 participants had a mean age of 52.1 ± 11.4 years and a mean body mass index of 28.9 ± 5.8 kg/m2 (calculated as weight in kilograms divided by height in meters squared). Mean preoperative PVR was 25.8 ± 29.9 mL. Most participants had surgery that included a midurethral sling (137/160, 85.6%). Thirty-four (34/160, 21.3%) participants met criteria for postoperative urinary retention. The optimal recovery room TOV thresholds to predict postoperative urinary retention were PVR ≥87 mL (sensitivity 96.8%, specificity 60.0%), voided volume ≤ 150 mL (sensitivity 83.9%, specificity 72.3%), and sFOS ≤60% (sensitivity 100%, specificity 50.8%). Voided volume ≤ 150 mL had greater diagnostic accuracy than PVR ≥100 mL (156.2 vs 151.8). In this cohort, PVR ≥87 mL, voided volume ≤ 150 mL, and sFOS ≤60% had optimal diagnostic accuracy for postoperative urinary retention.

Modification of McGill Oximetry Score in Improving the Diagnostic Capability of Paediatric OSA.

This study aimed to devise a modified oximetry scoring system and calculate its diagnostic accuracy in detecting paediatric obstructive sleep apnoea syndrome (OSAS). This prospective diagnostic accuracy study was divided into two phases. The study was conducted at a quaternary teaching hospital. Polysomnograms performed from 1 April 2014 to 31 December 2021 were included. In Phase 1, the parameters of 95 oximetry trend graphs were evaluated, and a modified scoring system was constructed. In Phase 2, the modified scoring system was employed in 272 oximetry trend graphs, and its diagnostic accuracy was determined. A logistic regression model was used to assess the ability of each scoring system to predict paediatric OSAS. A total of 367 patients were recruited. In Phase 1, a four-tier severity classification system was constructed. In Phase 2, its diagnostic accuracy was found to be 53.3% sensitive, 97% specific, with positive predictive value of 98.5% and negative predictive value of 34.6%. The lowest detectable apnoea-hypopnoea index (AHI) was 4.5. The inter-rater reliability calculated was 80%. Logistic regression was applied to assess associations of the modified McGill score (MMS) or McGill oximetry score (MOS) with OSAS. The area under the receiver operating characteristic curve was higher for the MMS than for MOS (0.78 [95% CI 0.73-0.84] vs. 0.59 [95% CI 0.51-0.66]). This study demonstrated that our modified scoring system had increased sensitivity at detecting OSAS at a much lower AHI and showed a much greater ability to predict paediatric OSAS.

A loop-mediated isothermal amplification test for yaws: a multi-country diagnostic accuracy evaluation

To meet the WHO target of eradicating yaws by 2030, highly sensitive and specific diagnostic tools are needed. A multiplex Treponema pallidum-Haemophilus ducreyi loop-mediated isothermal amplification (TPHD-LAMP) test holds promise as a near-patient diagnostic tool for yaws and H ducreyi. We conducted a prospective evaluation in Cameroon, Côte d'Ivoire, Ghana, and the Republic of the Congo to determine the diagnostic accuracy of the TPHD-LAMP test, as well as to assess its acceptability, feasibility, and cost. Active case searching within schools and communities was used to locate participants with clinically suspicious laws-like lesions. Individuals with serologically confirmed active yaws provided paired lesion swabs between March, 2021, and April, 2023. For each participant, one swab was tested with the TPHD-LAMP at a local district laboratory and the other with reference quantitative PCR (qPCR) tests conducted at national reference laboratories. The primary outcome was TPHD-LAMP test sensitivity and specificity compared with qPCR. Laboratory technicians were interviewed using a multiple-choice survey to gauge acceptability and feasibility of the TPHD-LAMP test. Costs of each test were calculated. Of 3085 individuals with at least one suspected yaws lesion, 531 (17%) were serologically confirmed. We enrolled 493 participants with seropositive yaws and a further 32 with negative serology. The sensitivity of the TPHD-LAMP test for detecting T pallidum was 63% (95% CI 56-70) and the specificity was 66% (95% CI 61-71). Sensitivity and specificity for T pallidum improved to 73% (63-82; p=0·0065) and 75% (68-80; p=0·0003), respectively, in H ducreyi-negative samples. Interviews highlighted challenges in user-friendliness and practicality of the TPHD-LAMP test. The cost of the test per sample was one third of that of qPCR, although the TPHD-LAMP test entailed higher costs to establish the assay. This was the first multi-country diagnostic evaluation of a molecular test for yaws. The TPHD-LAMP testing, in its current form, falls short of the WHO target product profile criteria for yaws diagnostics. These findings highlight the importance of assessing new diagnostics in real-world conditions to ensure their suitability for programmatic use. The EDCTP2 programme supported by the EU.

Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis

Infectious keratitis (IK) is the leading cause of corneal blindness globally. Deep learning (DL) is an emerging tool for medical diagnosis, though its value in IK is unclear. We aimed to assess the diagnostic accuracy of DL for IK and its comparative accuracy with ophthalmologists. In this systematic review and meta-analysis, we searched EMBASE, MEDLINE, and clinical registries for studies related to DL for IK published between 1974 and July 16, 2024. We performed meta-analyses using bivariate models to estimate summary sensitivities and specificities. This systematic review was registered with PROSPERO (CRD42022348596). Of 963 studies identified, 35 studies (136,401 corneal images from >56,011 patients) were included. Most studies had low risk of bias (68.6%) and low applicability concern (91.4%) in all domains of QUADAS-2, except the index test domain. Against the reference standard of expert consensus and/or microbiological results (seven external validation studies; 10,675 images), the summary estimates (95% CI) for sensitivity and specificity of DL for IK were 86.2% (71.6-93.9) and 96.3% (91.5-98.5). From 28 internal validation studies (16,059 images), summary estimates for sensitivity and specificity were 91.6% (86.8-94.8) and 90.7% (84.8-94.5). Based on seven studies (4007 images), DL and ophthalmologists had comparable summary sensitivity [89.2% (82.2-93.6) versus 82.2% (71.5-89.5); P=0.20] and specificity [(93.2% (85.5-97.0) versus 89.6% (78.8-95.2); P=0.45]. DL models may have good diagnostic accuracy for IK and comparable performance to ophthalmologists. These findings should be interpreted with caution due to the image-based analysis that did not account for potential correlation within individuals, relatively homogeneous population studies, lack of pre-specification of DL thresholds, and limited external validation. Future studies should improve their reporting, data diversity, external validation, transparency, and explainability to increase the reliability and generalisability of DL models for clinical deployment. NIH, Wellcome Trust, MRC, Fight for Sight, BHP, and ESCRS.

Minimum Fascia-Tumour Distance for Differentiating Deep Lobe From Superficial Lobe Benign Parotid Tumours: A Retrospective Study and Meta-Analysis.

This study was designed to evaluate the diagnostic efficacy of the minimum fascia-tumour distance (MFTD) in distinguishing deep-lobe benign parotid tumours from superficial-lobe tumours through both an original study and a meta-analysis. In this study, we performed a retrospective analysis of data from 91 patients who had been diagnosed with benign parotid tumours. The MFTD values were sourced from preoperative ultrasound examinations. The locations of these tumours were confirmed through surgical findings. We assessed the diagnostic accuracy of MFTD by utilising receiver operating characteristic (ROC) curves. Additionally, we conducted a systematic review of the pertinent literature and performed a diagnostic meta-analysis to ascertain the overall diagnostic efficacy of MFTD in identifying benign parotid tumours. Patients with tumours in the deep lobe had a significantly greater MFTD than patients with tumours in the superficial lobe. Using a cutoff value of 3.50 mm for MFTD, we found an AUC of 0.93, a sensitivity of 81.8%, and a specificity of 98.8%. Our meta-analysis included seven studies covering a total of 1689 tumours. The pooled values for sensitivity, specificity, and diagnostic odds ratio (OR) of MFTD were 81.0%, 89.0%, and 32.2, respectively. The AUC of the summarised ROC curve of MFTD was 0.90. The MFTD demonstrated reliable diagnostic accuracy in identifying deep-lobe benign parotid tumours and may be incorporated into standard evaluations before parotidectomy.

Diagnostic Accuracy of Lung Ultrasonography Compared to Chest Radiography, BNP and Physical Examination in Patients with Dyspnea Suggestive of Pulmonary Edema: A Systematic Review and Meta-Analysis

Background: Pulmonary edema (PE) is the result of an abrupt increase in hydrostatic pressure in the pulmonary capillaries that leads to leakage of fluid through microvascular endothelial cells. This leads to a disruption of gas exchange in the lungs. Aims: This meta-analysis aimed to determine the diagnostic accuracy of lung ultrasonography (LUS) in pulmonary edema. Methods: A systematic search was conducted using a strategy based on these search terms (Lung ultrasonography, pulmonary edema, diagnostic accuracy); we searched PubMed, Google Scholar, and the Cochrane Library. Out of 1029, 14 prospective cross-sectional and observational studies with 2239 patients who reported the sensitivity and specificity of lung ultrasonography in diagnosing pulmonary edema were selected. For inclusion and data extraction, an independent review of citations was carried out. The data obtained were analyzed using SPSS, RevMan 5.3, and Stata 14.0 software. A quality assessment was conducted using the QUADAS-2 tool. The reference gold standard was the final clinical diagnosis according to chest radiography, B-type natriuretic peptide, and/or physical examination in dyspneic patients. Results: The overall sensitivity and specificity of lung ultrasonography in the diagnosis of pulmonary edema were 0.86 (95% CI, 0.81-0.90) and 0.91 (95% CI, 0.90-0.93), respectively, with a Younden index of 77.8%. The area under the receiver operating characteristic (ROC) curve was 0.889. Conclusion: The overall diagnostic odds ratio was 68.86. The results of this meta-analysis suggest that lung ultrasonography is an effective non-invasive technique in the diagnosis of acute pulmonary edema with rapid bedside examination and immediate interpretation.

Predictive nomograms and an algorithm for managing patients with probable Meniere's disease

PurposeTo explore the efficacy of diagnostic tests in accurately reclassifying patients initially diagnosed with probable Meniere's disease (MD) into either definite or non-MD categories. Materials and methodsA retrospective cohort study was conducted at a neurotology clinic between 1/2016 and 5/2022. Patients underwent a battery of tests, from which sensitivity, specificity, positive and negative predictive values, as well as positive and negative likelihood ratios, were calculated. Additionally, prediction nomograms were developed. ResultsOf the 69 patients, 25 (36.2 %) were initially classified as definite MD, 21 (30.4 %), probable MD, and 23 (33.4 %) non-MD. The mean follow-up was 3.5 years. The sensitivity of electrocochleography (ECochG) was the highest (92 %), with a negative likelihood ratio of 15 %. Magnetic resonance imaging (MRI) with MD-protocol had the highest specificity (100 %), with a positive likelihood ratio of 100 %. Videonystagmography, video head impulse test, and cervical vestibular-evoked myogenic potentials, had lower sensitivity and specificity. We were able to reclassify 18 (86 %) patients with probable MD: 12 (57 %) were diagnosed with definite MD, and 6 (29 %) were diagnosed with non-MD, consistent with their clinical course. ConclusionsThe combination of ECochG and MRI with MD-protocol provides the most reliable approach to reclassify patients with Probable MD, ensuring a precise and accurate diagnosis. Vestibular tests express the functional status of the labyrinth and may not be reliable. Our findings provide valuable insights into clinical decision-making for patients with Probable MD and raise the consideration of additional diagnostic tests as supplementary to the existing clinical-only diagnosis criteria.

Performance du score de probabilité à 4 niveaux 4PEPS pour le diagnostic de l'embolie pulmonaire dans une population d'Afrique subsaharienne : données du Registre des Embolies Pulmonaires du Centre Hospitalier Universitaire de Bogodogo, Burkina Faso

ObjectiveTo evaluate the performance of the 4PEPS score in the diagnosis of pulmonary embolism at the University Hospital of Bogodogo from January 1, 2021 to July 31, 2023. MethodologyThis was a cross-sectional descriptive and analytical diagnostic study, running from January 1, 2021 to July 31, 2023. It took place in the infectious and tropical diseases departments, including a pneumology unit and a cardiology unit, of the Bogodogo University Hospital. Patients of both sexes with suspected pulmonary embolism who had undergone CT scan were included. The 4PEPS score was calculated and dichotomized into probable and improbable. It thus constituted the diagnostic test. CT scan was the gold standard. The accuracy of the diagnostic test was judged by the area under the ROC curve. An area under the curve between 0.70 and 1 would mean that the score was moderately informative to perfect. ResultsOur study included 472 patients with suspected pulmonary embolism out of a total population of 1228 patients. Hospital prevalence was 21.7%. The mean age of patients was 54.3 years. Females accounted for 52.1% of cases, with a sex ratio of 0.93. The prevalence of pulmonary embolism in the different probability levels of the 4PEPS score was 13.3% for the very low level, 11.7% for the low level, 84.6% for the intermediate level and 93.3% for the high level. Sensitivity and specificity were 92.1% and 86.82% respectively. The positive and negative predictive values were 90.1% and 89.4% respectively. The area under the ROC curve was 0.91. ConclusionIn our study, the 4PEPS score showed good negative and positive predictive values. The use of this score will enable practitioners faced with diagnostic difficulties to make therapeutic decisions, reducing inappropriate prescriptions for thoracic angioscan.

Factors influencing the initiation of biologic therapy in children with severe asthma: Results of the pediatric asthma noninvasive diagnostic approaches (PANDA) study.

Despite the availability of biologics for severe pediatric asthma, real-life studies reporting on drivers behind initiating biologics and their alignment with the Global Initiative for Asthma (GINA) recommendations are lacking. We performed analysis within the pediatric asthma noninvasive diagnostic approaches study, a prospective cohort of 6- to 17-year-old children with severe asthma. Information was collected on demographic factors, symptom control, treatment, comorbidities, and diagnostic tests from medical records and questionnaires. We divided patients into "starters" or "nonstarters" based on the clinical decision to initiate biologics and performed multivariate logistic regression analysis to identify drivers behind initiating therapy. Additionally, we assessed patient suitability for biologics according to key factors in the GINA recommendations: Type 2 inflammation, frequency of exacerbations, and optimization of treatment adherence. In total, 72 children (mean age 11.5 ± 3.0 years, 65.3% male) were included (13 starters). Initiation of biologics was associated with a higher GINA treatment step (adjusted odds ratio's [aOR] = 5.0, 95%CI 1.33-18.76), steroid toxicity (aOR = 21.1, 95%CI 3.73-119.91), frequency of exacerbations (aOR = 1.6, 95%CI 1.10-2.39), improved therapy adherence (aOR = 1.7, 95%CI 1.10-2.46), Caucasian ethnicity (aOR = 0.20, 95%CI 0.05-0.80), ≥1 allergic sensitization (aOR = 0.06, 95%CI 0.004-0.97), and allergic rhinitis (aOR = 0.13, 95%CI 0.03-0.65). Furthermore, steroid toxicity was identified as an important factor for deviation from the current recommendations on biologic prescription. We identified multiple drivers and inhibitors for initiating biologics, and showed the clinical need for biologics in severe pediatric asthmatics suffering from steroid toxicity. These findings may help refine asthma management guidelines.

An effective and specific cancer theranostic nanoplatform with NIR-II fluorescence imaging-guided photothermal/chemodynamic therapy

Nanomaterial-based second near-infrared region (NIR-II) fluorescent probes have deep tissue penetration. However, it is limited by low catalytic efficiency and specific recognition in the tumor microenvironment (TME). Herein, Ag2S-Cu(II)-Cu2-xSe diagnostic nanoparticles have been established by Cu2-xSe and Ag2S QDs, which can serve as diagnostic agents. They can improve the precise diagnosis of cancer via a NIR-II imaging-guided multimodal photothermal/chemodynamic therapy strategy under near-infrared (808 nm) light irradiation. Under neutral conditions, the fluorescence of Ag2S-Cu(II)-Cu2-xSe nanoplatforms is in the “off” mode, which is selectively turned on only in the tumor microenvironment. Ag2S-Cu(II)-Cu2-xSe NPs not only emit fluorescence specifically in the TME but also have efficient photothermal conversion properties. Significantly, Ag2S-Cu(II)-Cu2-xSe has experimentally demonstrated a strong combined therapeutic effect and can consume excess intracellular glutathione (GSH) through redox reactions. This work presents an effective strategy for achieving precise and safe multifunctional nanodiagnostic platforms.

Chronological age estimation based on dental mineralization for Syrian population.

Dental age assessment based on evaluating dental mineralization status is one of the most common methods used in forensic practice. The aim of this study is to enhance the accuracy of age diagnostics and provide reference data from the Syrian population for forensic application. After several selection steps, a total of 280 orthopantomograms (OPGs) from 140 males and 140 females from the Syrian population divided into 14 age groups between 12 and 25 years were analysed. Based on Demirjian's classification system, the mineralization stages of third molars (18, 28, 38 and 48) as well as lower second molars (37 and 47) were evaluated. Statistical investigations and evaluations were carried out to estimate the marginal probabilities of the subjects having attained ages 14 and 18 by generalized estimating equation models. Our results show that no significant differences can be revealed in the mineralization status with respect to jaw side and sex. In the Syrian population, third molars showing mineralization stage G provide evidence of reaching the age of 14 years with the highest standard of proof ("beyond reasonable doubt"). A completed mineralization in lower second molars (stage H) provides very high marginal probabilities (more than 90%) of the subjects having attained age 14 years. Nevertheless, this cannot exclude an age under 14 years. For the age threshold of 18 years, third molars showing incomplete root development (G dental stage or lower) are associated with a low probability (less than 40%) of the subject having reached 18 years of age. A person's probability of having attained 18 years of age is very high (82- 95%) when the roots of third molars are fully developed (stage H). Nevertheless, third molars at stage H do not conclusively exclude an age under 18 years.

Diagnostic and prognostic accuracy of exosomal miRNA-based signature for endometrial cancer

Diagnostic and prognostic accuracy of exosomal miRNA-based signature for endometrial cancer

Diagnostic and prognostic accuracy of exosomal miRNA-based signature for endometrial cancer

Diagnostic and prognostic accuracy of exosomal miRNA-based signature for endometrial cancer

The Limping Child: A Guide for the Trainees.

Limping is a common presenting complaint in children. Despite this, it remains to be a diagnostic challenge for treating physicians due to an expanded list of etiologies. It arises from a spectrum of disorders, ranging from physiological variations of gait at different stages of development to systemic causes, such as inflammatory diseases or musculoskeletal infections. On rare occasions, non-musculoskeletal causes could result in limping. The diagnostic challenge increases in younger age children where a detailed physical examination that helps identify the exact source of pathology may not be possible. In older patients who have a well-developed gait cycle, the physical assessment might be easier. Clinical assessment in a child presenting with a limp includes gait analysis, which is essential to guide the appropriate request of diagnostic laboratory tests and imaging studies. In this paper, we provide a practical guide for a trainee in General Pediatric and Pediatric Rheumatology on an approach to a limping child, aiming to identify the common causes of limping and to describe normal and abnormal gait cycles. We also discuss other diagnostic considerations in the assessment of these children.

Enhancing early detection of autistic spectrum disorder in children using machine learning approaches

Diagnosing Autism Spectrum Disorder (ASD) presents a multifaceted challenge, demanding accurate and efficient screening methods. Applying machine learning techniques offers a promising avenue for enhancing diagnostic accuracy and efficiency. This research investigates the efficiency of machine learning in distinguishing individuals with ASD from those without, utilizing a comprehensive dataset comprising screening questions, demographic factors, and ASD related diagnostic classifications. We applied chi-square feature selection technique and also tested Random Forest, Logistic Regression, Gradient Boosting Classifier, and Extra Trees Classifier. Each model showed optimal performance and exhibit high precision, recall, and F1-score for both ASD-positive and ASD-negative instances. Additionally, AUROC curves further validated the models’ exceptional discriminatory abilities, with exceptional results. Our findings highlight the potential of machine learning algorithms for enhancing ASD diagnosis accuracy and efficiency in clinical settings. Further research and validation on larger datasets are required to understand the importance of machine learning methods in ASD diagnosis.

Assessing the Value of 68Ga-FAPI PET/CT in Gastric Mucinous Adenocarcinoma or Signet Ring Cell Carcinoma.

Purpose To investigate the clinical impact and prognostic value of gallium 68 (68Ga)-labeled fibroblast activation protein inhibitor (FAPI) PET/CT in gastric mucinous adenocarcinoma (MAC) and signet ring cell carcinoma (SRCC). Materials and Methods Eighty-six participants with newly diagnosed or recurrent gastric MAC or SRCC were prospectively enrolled from April 2021 to October 2021 and underwent both fluorine 18 (18F) fluorodeoxyglucose (FDG) PET/CT and 68Ga-FAPI PET/CT. The sensitivity, specificity, and accuracy of the two scans in primary and metastatic tumors were evaluated using the McNemar test. Changes of treatment strategies were recorded to compare the treatment management value of the two PET/CT scans. The maximum standardized uptake value (SUVmax) and peritoneal cancer index (PCI) were recorded for survival analysis. Progression-free survival (PFS) was defined as the time interval from the date of PET/CT scans to the date of disease progression. Results Eighty-six participants (median age, 62 years [IQR, 45-78 years]; 49 female) were evaluated. 68Ga-FAPI PET/CT showed higher diagnostic accuracy in detecting involved lymph nodes (87% [212 of 244] vs 71% [173 of 244], P < .001) and peritoneal metastases (96% [70 of 73] vs 55% [40 of 73], P < .001) than 18F-FDG PET/CT. Twenty-six participants (30% [26 of 86]) had treatment changes due to more accurate diagnosis with 68Ga-FAPI PET/CT. Additionally, the 68Ga-FAPI PCI was an independent predictor for PFS (hazard ratio, 6.9; 95% CI: 2.1, 23.1; P = .002). Conclusion 68Ga-FAPI PET/CT had higher accuracy in diagnosis of gastric MAC/SRCC compared with 18F-FDG PET/CT and demonstrated the potential to improve treatment strategies and predict prognosis. Keywords: PET/CT, Mucinous Adenocarcinoma, Signet Ring Cell Carcinoma, Oncology, Abdomen/GI, Molecular Imaging Supplemental material is available for this article. © RSNA, 2024.

Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph.

Artificial intelligence (AI) could be used as an automatically diagnosis method for dental disease due to its accuracy and efficiency. This research proposed a novel convolutional neural network (CNN)-based deep learning (DL) ensemble model for tooth position detection, tooth outline segmentation, tooth tissue segmentation, periodontal bone loss and periodontitis stage prediction using dental panoramic radiographs. The dental panoramic radiographs of 320 patients during the period January 2020 to December 2023 were collected in our dataset. All images were de-identified without private information. In total, 8462 teeth were included. The algorithms that DL ensemble model adopted include YOLOv8, Mask R-CNN, and TransUNet. The prediction results of DL method were compared with diagnosis of periodontists. The periodontal bone loss degree deviation between the DL method and ground truth drawn by the three periodontists was 5.28%. The overall PCC value of the DL method and the periodontists' diagnoses was 0.832 (P <​ 0.001). ​The ICC value was 0.806 (P <​ 0.001). The total diagnostic accuracy of the DL method was 89.45%. The proposed DL ensemble model in this study shows high accuracy and efficiency in radiographic detection and a valuable adjunct to periodontal diagnosis. The method has strong potential to enhance clinical professional performance and build more efficient dental health services. The DL method not only could help dentists for rapid and accurate auxiliary diagnosis and prevent medical negligence, but also could be used as a useful learning resource for inexperienced dentists and dental students.

A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective.

Low-dose computed tomography (LDCT) scans are essential in reducing radiation exposure but often suffer from significant image noise that can impair diagnostic accuracy. While deep learning approaches have enhanced LDCT denoising capabilities, the predominant reliance on objective metrics like PSNR and SSIM has resulted in over-smoothed images that lack critical detail. This paper explores advanced deep learning methods tailored specifically to improve perceptual quality in LDCT images, focusing on generating diagnostic-quality images preferred in clinical practice. We review and compare current methodologies, including perceptual loss functions and generative adversarial networks, addressing the significant limitations of current benchmarks and the subjective nature of perceptual quality evaluation. Through a systematic analysis, this study underscores the urgent need for developing methods that balance both perceptual and diagnostic quality, proposing new directions for future research in the field.

Artificial intelligence applications in cataract and refractive surgeries.

This review highlights the recent advancements in the applications of artificial intelligence within the field of cataract and refractive surgeries. Given the rapid evolution of artificial intelligence technologies, it is essential to provide an updated overview of the significant strides and emerging trends in this field. Key themes include artificial intelligence-assisted diagnostics and intraoperative support, image analysis for anterior segment surgeries, development of artificial intelligence-based diagnostic scores and calculators for early disease detection and treatment planning, and integration of generative artificial intelligence for patient education and postoperative monitoring. The impact of artificial intelligence on cataract and refractive surgeries is becoming increasingly evident through improved diagnostic accuracy, enhanced patient education, and streamlined clinical workflows. These advancements hold significant implications for clinical practice, promising more personalized patient care and facilitating early disease detection and intervention. Equally, the review also highlights the fact that only some of this work reaches the clinical stage, successful integration of which may benefit from our focus.

89Zr-Labeled DFO@Durvalumab-HSA nanoparticles: In vitro potential for triple-negative breast cancer.

This study presents the development and evaluation of a DFO@mAb-NP (DFO@Durvalumab-HSA-DTX nanoparticle) nanoplatform for imaging in triple-negative breast cancer (TNBC). The nanoplatform demonstrated significant changes postconjugation with DFO, evidenced by increased particle size from 178.1 ± 5 nm to 311 ± 26 nm and zeta potential alteration from -31.9 ± 3 mV to -40.5 ± 0.8 mV. Fourier-transform infrared spectroscopy and ultraviolet spectral analyses confirmed successful DFO conjugation, with notable shifts in peak wavelengths. High labeling efficiency was achieved with 89Zr, as indicated by thin layer radio chromatography and high-performance liquid radio chromatography results, with labeling efficiencies of 98 ± 2% for 89Zr-DFO@mAb and 96 ± 3% for 89Zr-DFO@mAb-NP. The nanoplatforms maintained stability over 24 h, showing less than 5% degradation. Lipophilicity assays revealed logP values of 0.5 ± 0.03 for 89Zr-DFO@mAb-NP and 0.98 ± 0.2 for 89Zr-DFO@mAb, indicating a higher lipophilic tendency in the radiolabeled Durvalumab. Cell uptake experiments showed an initial high uptake in MDA-MB-468 cells (45.1 ± 3.2%), which decreased over time, highlighting receptor-specific interactions. These comprehensive findings suggest the promising potential of the DFO@mAb-NP nanoplatform for targeted imaging in TNBC, with implications for improved diagnostic accuracy and treatment strategies.