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At least 19 recordsLinked to original sources
PubMed1979

[Computed tomography of soft tissue sarcomas (author's transl)].

CT findings in 50 patients with histologically verified soft tissue sarcomas are presented. In nearly all the cases the cross-sectional view and the higher resolution of density make it possible to determine exactly location of the tumors, their size and relationship to adjacent structures. Thus CT opens a new dimension, which is of great value for staging and therapy treatment planning. The high rank of CT in the follow-up after therapy and the detection of recurrent tumor is demonstrated. The contribution of CT, however, to the anatomic characteristics and its prospective value with the regard to soft tissue tumor is less important. Further limits of the method are discussed.

Humans↗
PubMed1975

Soft-hard tissue correlations and computer drawings for the frontal view.

Hard and soft tissue measurements were obtained for nine Caucasian women. Based on these measurements, regression formulas were derived to approximate the soft tissue covering based on hard tissue data. The results are presented iconically by computer drawings. The preliminary data presented would suggest a definite predictable influence exerted by the facial skeleton on the position of landmarks in the overlying soft tissue.

Adult↗
PubMed1979

[Computer tomography for rare soft tissue tumours of the extremities (author's transl)].

Five patients with undiagnosed soft tissue masses in the extremities were examined and in two a pathological diagnosis could be made. One was an extensive, invasive fibroma (desmoid) 22 cm long which could be followed from the thigh almost into the pelvis. It was sharply demarkated form the surrounding muscles and of higher density. The second case was a 12 cm long cavernous haemangioma in the semi-membranosus muscle. This was originally hypo-dense, but showed marked increase in its density after the administration of contrast.

Adolescent↗
PubMed1979

Computed tomography in bone and soft tissue pathology of the extremities.

Forty patients suffering from various pathological conditions of the extremities were examined by computed tomography (CT) and the results were correlated with conventional imaging techniques: plain radiography, conventional tomograms, angiograms, and radionuclide scanning. The patients' ages ranged from 4 to 80 years. The study included cases of osteomyelitis, benign and malignant bone and soft tissue tumors, metabolic disorders, and a case of pulmonary osteoarthropathy. We were able to follow progression and/or regression of osteomyelitis by recording the changes in the attenuation coefficient of the medullary cavity and also were able to determine the extent of the malignant infiltration of the marrow cavity. Contrast enhancement was valuable in demonstrating the vascularity of soft tissue tumors and in determining the extension of primary bone lesions.

Adolescent↗
PubMed1979

Use of computed tomography in diagnosis of soft-tissue tumors.

Soft-tissue tumors can evade the usual diagnostic methods. In the case presented, computed tomography proved to be the best diagnostic tool in demonstrating a malignant extranodal lymphoma. Computed tomography is useful not only in detecting a tumor but also in determining its extent. This aids the surgeon in selecting and conducting the most appropriate operation.

Abdominal Neoplasms↗
PubMed2026

Artificial Intelligence for Colorectal Surgeons-Part II: Research Applications, Challenges in Adoption, and Practical Resources.

BACKGROUND: This is part II of a 2-part series examining artificial intelligence in colorectal surgery. Part I established foundational concepts and clinical applications. Implementation, however, requires understanding research methodologies, available resources, and the specific challenges currently limiting widespread adoption. These topics are the focus of part II. OBJECTIVE: To examine artificial intelligence's transformation of surgical research, provide practical implementation resources, address adoption challenges, and explore future directions in colorectal surgery. METHODS: Comprehensive literature review focusing on artificial intelligence research methodology, implementation barriers, educational resources, and emerging technologies relevant to colorectal surgeons. RESULTS: Artificial intelligence streamlines clinical trial design through predictive modeling and natural language processing, reducing enrollment challenges that contribute to failed or inadequate trial accrual. Machine learning enables heterogeneity analysis within clinical trials, identifying treatment-responsive subgroups. Foundation models unlock analysis of unstructured electronic health record data at scale. Professional societies and universities offer specialized artificial intelligence education programs, with open-access data sets facilitating research participation. However, implementation faces multifaceted challenges: technical infrastructure demands, with real-time processing requiring dedicated graphics processing unit clusters; regulatory frameworks struggling with continuously evolving algorithms; undefined liability distribution for artificial intelligence-assisted decisions; algorithmic bias risking health care disparities; and the "black box" problem limiting clinical trust. Economic barriers include substantial initial costs without clear reimbursement pathways. Future directions include multimodal artificial intelligence integrating imaging, genomics, and histopathology; cognitive robotic systems with real-time decision support; digital twin technology for patient-specific surgical simulation; and global surgical artificial intelligence networks enabling distributed learning across institutions. CONCLUSIONS: Although artificial intelligence offers transformative potential for colorectal surgery research and practice, successful implementation requires addressing technical, regulatory, ethical, and economic challenges. The surgeon's evolving role demands both traditional expertise and computational fluency. Future advances in multimodal integration, autonomous systems, and global collaboration will fundamentally reshape surgical practice but will require thoughtful implementation prioritizing patient benefit and clinical value.

Humans↗
PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗
PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗
PubMed1979

Computed tomography evaluation of fatty tumors of the somatic soft tissues: clinical utility and radiologic-pathologic correlation.

The role of computed tomography (CT) in the evaluation of fatty tumors of the somatic soft tissues was investigated. Six surgically proven cases of fatty tumors were studied preoperatively by CT and standard radiographic means--conventional radiographs, xeroradiography, and angiography. Our case material included a simple lipoma, two infiltrative lipomas, an angiolipoma, and two liposarcomas. The radiologic-pathologic correlation was evaluated with respect to the various imaging modalities. The unique tissue characteristics of fatty tumors makes them particularly adaptable to CT scanning. In addition to its ability to define accurately tissue densities, the facility of CT in depicting depth, size, and extent of the lesion in the axial plane was found to be most useful in the preoperative evaluation of our case material.

Adipose Tissue↗
PubMed1978

Computed tomography of the paranasal sinuses and face: part I. Normal anatomy.

With the ability to image both bone and soft tissue structures, computed tomography (CT) is capable of visualizing many normal anatomical structures of the paranasal sinuses and face not seen with other radiological techniques. The superficial and deep fat planes, all of the muscles of mastication, and many of the facial muscles are readily identified. The extraocular muscles, optic nerves, and globes are clearly seen. The purpose of this report is to review the normal anatomy of the paranasal sinuses and face imaged by CT in both the transverse and coronal planes.

Face↗
PubMed1979

[Biometric studies in the cytological diagnosis of soft tissue tumors].

Introducing of quantitative signs (the number of cells on the given area, size of cells, nuclei, optic and spectral characteristics) makes the cytologic diagnosis much more objective. Karyometric studies conducted by the author (the determination of nuclei volume) on cytological smears, obtained from 16 patients with soft tissue tumors, indicated some difference in nuclei size of various histotypes. The studies have evidenced that the method of karyometry associated with cytophotometry will offer the pre-conditions for using karyometry in the practice of pathocytologists and facilitate the finding of algorithms for computing analysis of soft tissue neoplasms.

Cell Nucleus↗
PubMed1978

Computed tomography of the orbit with special emphasis on coronal sections: Part II. Pathological anatomy.

Visualization of orbital soft tissue structures by computed tomography in direct coronal and axial studies is extremely useful in diagnosis. Direct enlargement viewing of scans has disclosed minute anatomical details. This study reviews some of our experiences in the investigation of a variety of lesions within the orbit and attempts, in particular, to illustrate the value of direct coronal studies.

Adult↗
PubMed2026

A reinforcement learning-enhanced fuzzy multi-objective equilibrium optimization framework for multiple sequence alignment.

Multiple sequence alignment (MSA) is a fundamental task in bioinformatics, underpinning comparative genomics, structural analysis, and evolutionary inference. However, MSA remains a challenging multi-objective optimization problem due to the need to simultaneously maximize alignment accuracy, preserve conserved regions, and control gap proliferation, particularly in large and heterogeneous sequence collections. In this work, we propose MOFSACEO-MSA, a novel hybrid optimization framework for multiple sequence alignment that integrates a fuzzy multi-objective evaluation scheme with the Equilibrium Optimizer (EO) and a Soft Actor-Critic (SAC)-based adaptive control mechanism. The proposed framework formulates MSA as a dynamic multi-objective optimization problem, in which alignment quality is assessed using complementary residue-level and column-level criteria, including Sum-of-Pairs score, column conservation, entropy, and gap statistics. Fuzzy membership functions are employed to harmonize competing objectives into a unified optimization landscape, while EO provides robust global exploration. To further enhance adaptability, SAC dynamically regulates key EO parameters during the search process, enabling an effective balance between exploration and exploitation across datasets of varying size and heterogeneity. Extensive experiments werew conducted on diverse biological sequence datasets, with a primary focus on RNA benchmarks, including structured families from Rfam, large-scale repositories from RNAcentral and GenBank, and organism-specific tRNA datasets from GtRNAdb. Comparative evaluations against classical alignment tools (ClustalW, MAFFT, MUSCLE, PRANK, KAlign, and T-Coffee), metaheuristic methods (SAGA, Sequoya and EAFSA), and a reinforcement learning-based approach (RLALIGN) demonstrate that MOFSACEO-MSA consistently achieves competitive or superior Sum-of-Pairs scores while significantly reducing gap proportions and maintaining compact alignment lengths. Notably, the proposed framework exhibits improved robustness on large and highly heterogeneous datasets, where existing methods often suffer from excessive gap insertion or unstable convergence. Overall, MOFSACEO-MSA provides a flexible and extensible optimization paradigm that effectively bridges evolutionary search and reinforcement learning for high-quality multiple sequence alignment, with demonstrated effectiveness on challenging RNA alignment tasks.

Sequence Alignment↗