Advanced CKD detection through optimized metaheuristic modeling in healthcare informatics.

Data categorization is a top concern in medical data to predict and detect illnesses; thus, it is applied in modern healthcare informatics. In modern informatics, machine learning and deep learning models have enjoyed great attention for categorizing medical data and improving illness detection. However, the existing techniques, such as features with high dimensionality, computational complexity, and long-term execution duration, raise fundamental problems. This study presents a novel classification model employing metaheuristic methods to maximize efficient positives on Chronic Kidney Disease diagnosis. The medical data is initially massively pre-processed, where the data is purified with various mechanisms, including missing values resolution, data transformation, and the employment of normalization procedures. The focus of such processes is to leverage the handling of the missing values and prepare the data for deep analysis. We adopt the Binary Grey Wolf Optimization method, a reliable subset selection feature using metaheuristics. This operation is aimed at improving illness prediction accuracy. In the classification step, the model adopts the Extreme Learning Machine with hidden nodes through data optimization to predict the presence of CKD. The complete classifier evaluation employs established measures, including recall, specificity, kappa, F-score, and accuracy, in addition to the feature selection. Data related to the study show that the proposed approach records high levels of accuracy, which is better than the existing models.

BC-QNet: A quantum-infused ELM model for breast cancer diagnosis.

The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes.

Artificial intelligence improves resident detection of pediatric and young adult upper extremity fractures.

PURPOSE: We wished to evaluate if an open-source artificial intelligence (AI) algorithm ( https://www.childfx.com ) could improve performance of (1) subspecialized musculoskeletal radiologists, (2) radiology residents, and (3) pediatric residents in detecting pediatric and young adult upper extremity fractures. MATERIALS AND METHODS: A set of evaluation radiographs drawn from throughout the upper extremity (elbow, hand/finger, humerus/shoulder/clavicle, wrist/forearm, and clavicle) from 240 unique patients at a single hospital was constructed (mean age 11.3 years, range 0-22 years, 37.9% female). Two fellowship-trained musculoskeletal radiologists, three radiology residents, and two pediatric residents were recruited as readers. Each reader interpreted each case initially without and then subsequently 3-4 weeks later with AI assistance and recorded if/where fracture was present. RESULTS: Access to AI significantly improved area under the receiver operator curve (AUC) of radiology residents (0.768 [0.730-0.806] without AI to 0.876 [0.845-0.908] with AI, P < 0.001) and pediatric residents (0.706 [0.659-0.753] without AI to 0.844 [0.805-0.883] with AI, P < 0.001) in identifying fracture, respectively. There was no evidence of improvement for subspecialized musculoskeletal radiology attendings in identifying fracture (AUC 0.867 [0.832-0.902] to 0.890 [0.856-0.924], P = 0.093). There was no evidence of difference between overall resident AUC with AI and subspecialist AUC without AI (resident with AI 0.863, attending without AI AUC 0.867, P = 0.856). Overall physician radiograph interpretation time was significantly lower with AI (38.9 s with AI vs. 52.1 s without AI, P = 0.030). CONCLUSION: An openly accessible AI model significantly improved radiology and pediatric resident accuracy in detecting pediatric upper extremity fractures.

Utility of Zero-Echo time sequence as an adjunct for MR evaluation of degenerative disease in the cervical spine.

OBJECTIVE: Determine the utility of ZTE as an adjunct to routine MR for assessing degenerative disease in the cervical spine. METHODS: Retrospective study on 42 patients with cervical MR performed with ZTE from 1/1/2022 to 4/30/22. Fellowship trained radiologists evaluated each cervical disc level for neural foraminal (NF) narrowing, canal stenosis (CS), facet arthritis (FA), and presence of ossification of the posterior longitudinal ligament (OPLL). When NF narrowing and CS were present, the relative contributions of bone and soft disc were determined and a confidence level for doing so was assigned. Comparisons were made between assessments on routine MR without and with ZTE. RESULTS: With ZTE added, bone contribution as a cause of NF narrowing increased in 47% (n = 110) of neural foramina and decreased in 12% (n = 29) (p = < 0.001). Bone contribution as a cause of CS increased in 25% (n = 33) of disc levels and decreased in 10% (n = 13) (p = 0.013). Confidence increased in identifying the cause of NF narrowing (p = < 0.001)) and CS (p = 0.009) with ZTE. The cause of NF narrowing (p = 0.007) and CS (p = 0.041) changed more frequently after ZTE was added when initial confidence in making the determination was low. There was no change in detection of FA or presence of OPLL with ZTE. CONCLUSION: Addition of ZTE to a routine cervical spine MR changes the assessment of the degree of bone involvement in degenerative cervical spine pathology.

Imaging of the Ankle Ligaments and Cartilage Injuries as an Aid to Ankle Preservation Surgery.

Ankle sprains are among the most common musculoskeletal injuries and can lead to ankle ligament and cartilage injuries. Imaging plays an important role in differentiating ligament injuries from other causes of ankle pain such as fractures, osteochondral lesions or tendon injuries that helps guide further management. Chronic untreated ankle ligamentous and cartilage injuries can further progress to ankle osteoarthritis, hence the need for timely diagnosis and treatment. Surgical treatment is often required in patients not responding to conservative treatment and ranges from repair and reconstruction procedures for ligament injuries to arthroscopic debridement and repair procedures for cartilage injuries. Cartilage defects and deficiency may be augmented depending on the extent of cartilage loss and associated subchondral changes on MRI. Awareness of operative techniques utilized is essential to interpret imaging findings in postoperative settings.

Bones and Soft-Tissue Tumors: Considerations for Postsurgical Imaging Follow-up.

This article discusses how the radiologist should handle the imaging for the post-treatment sarcoma patient. This includes reviewing the timing of surveillance after therapy and the type of therapy used for sarcoma in order to better understand the typical post-treatment changes on imaging versus sarcoma recurrence. The type of imaging is reviewed, especially, magnetic resonance imaging and the relevant sequences, as well as the appearance of post-treatment changes, sarcoma recurrence, and post-treatment complications.

Imaging Review of Subscapularis Tendon and Rotator Interval Pathology.

As the largest rotator cuff muscle, the subscapularis plays a major role in stabilizing the glenohumeral joint, in conjunction with surrounding rotator cuff structures. Injury to the subscapularis tendon can be isolated, but more commonly is seen in conjunction with supraspinatus tendon pathology. Injury can be associated with biceps pulley instability, superior labral anterior-posterior (SLAP) tears, humeral head subluxation, and anterosuperior and coracoid impingements. The involvement of the rotator interval can lead to what is called "the hidden lesion," due to its difficulty to diagnose during arthroscopy. Understanding the anatomical relations of the subscapularis tendon with the rest of the rotator cuff and rotator interval, as well as common patterns of injury that involve the subscapularis tendon, can aid in proper diagnosis of these injuries leading to prompt surgical repair. This review describes the anatomy of the subscapularis muscle and tendon, and the magnetic resonance imaging (MRI) patterns of subscapularis tendon injury.

Knee cartilage damage and concomitant internal derangement on MRI in athletes competing at the Rio de Janeiro 2016 Summer Olympics.

PURPOSE: To report the MRI patterns of knee cartilage damage and concomitant internal derangement in athletes participating at the Rio de Janeiro 2016 Olympic Games. METHODS: Knee MRIs obtained at the core imaging facility of the International Olympic Committee were blindly, retrospectively reviewed by a board-certified musculoskeletal radiologist for meniscal, ligamentous, and tendon abnormalities. Cartilage assessment was based on the modified Outerbridge criteria. RESULTS: Of 122 athletes who received a knee MRI, 64 (52.4 %) had cartilage damage. Cartilage damage was more prevalent in the patellofemoral compartment (52 athletes, 42.6 %), followed by lateral (23 athletes, 18.9 %) and medial tibiofemoral compartments (12 athletes, 9.8 %). Patellofemoral cartilage damage was most prevalent in beach-volleyball (100 %), followed by volleyball (8 athletes, 66.7 %) and weightlifting (7 athletes, 70 %). Patellofemoral cartilage damage was most prevalent with quadriceps (8 athletes, 72.7 %) and patellar tendinosis (11 athletes, 61.1 %). Medial and lateral tibiofemoral cartilage damage was significantly associated with medial (8 athletes, 29.6 %) and lateral meniscal tears (16 athletes, 55.2 %), respectively. There was a trend for the percentage of athletes with cartilage damage to increase with age. CONCLUSION: The majority of athletes at the 2016 Rio Summer Olympics who had a knee MRI showed cartilage damage. Patellofemoral compartment cartilage damage was most common and frequently observed in certain sports including volleyball, beach volleyball, and weightlifting. Overuse in these sports can contribute to patellofemoral cartilage damage and subsequent development of anterior knee pain. Cartilage damage was also observed with concomitant meniscal tears and older age.

Online sequential echo state network with sparse RLS algorithm for time series prediction.

Recently, the echo state networks (ESNs) have been widely used for time series prediction. To meet the demand of actual applications and avoid the overfitting issue, the online sequential ESN with sparse recursive least squares (OSESN-SRLS) algorithm is proposed. Firstly, the ℓ0 and ℓ1 norm sparsity penalty constraints of output weights are separately employed to control the network size. Secondly, the sparse recursive least squares (SRLS) algorithm and the subgradients technique are combined to estimate the output weight matrix. Thirdly, an adaptive selection mechanism for the ℓ0 or ℓ1 norm regularization parameter is designed. With the selected regularization parameter, it is proved that the developed SRLS shows comparable or better performance than the regular RLS. Furthermore, the convergence of OSESN-SRLS is theoretically analyzed to guarantee its effectiveness. Simulation results illustrate that the proposed OSESN-SRLS always outperforms other existing ESNs in terms of estimation accuracy and network compactness.

Multidetector Computed Tomography Imaging of Damage Control Surgery Patients.

Damage control surgery is a staged surgical procedure in a patient who has suffered penetrating or blunt abdominal traumatic injury with severe metabolic derangements. Multidetector computed tomography scanning is a vital tool for patient management in the damage control patient, providing many uses, including assessing the extent of traumatic injury, evaluating areas that were not surgically explored, evaluating for injuries that were missed during the initial surgery, and assessing the stability of surgical repair. Understanding the postsurgical multidetector computed tomography appearance of these patients can aid the radiologist in protocol optimization and provide immediate accurate diagnoses.