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Advancing cancer diagnosis and treatment: integrating image analysis and ai algorithms for enhanced clinical practice

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第4期58卷 1-25页

作者： Saeidnia, Hamid Reza Firuzpour, Faezeh Kozak, Marcin Majd, Hooman Soleymani Tarbiat Modares Univ TMU Dept Knowledge & Informat Sci Tehran Iran Babol Univ Med Sci USERN Off Babol Iran Babol Univ Med Sci Res Comm Babol Iran Univ Informat Technol & Management Rzeszow PL-35225 Rzeszow Poland Oxford Univ Hosp NHS Fdn Trust Churchill Canc Ctr Dept Gynaecol Oncol Oxford England

Cancer screening and diagnosis with the utilization of innovative Artificial Intelligence tools improved the treatment strategies and patients' survival. With the rapid development of imaging technologies and the rise of artificial intelligence (ai), there is a significant opportunity to improve cancer diagnostics through the combination of image analysis and ai algorithms. This article provides a comprehensive review of studies that have investigated the application of ai-assisted image processing in cancer diagnosis. We searched the Web of Science and Scopus databases to identify relevant studies published between 2014 and January 2024. The search strategy utilized targeted keywords such as cancer diagnostics, image analysis, artificial intelligence, and advanced imaging techniques. We limited the review to articles written in English and using ai-assisted image processing in cancer diagnosis. The results show that by leveraging machine learning algorithms, including deep learning, computer-aided diagnosis systems have been developed that are efficient in detecting tumors, thereby facilitating early cancer detection. Additionally, various authors have explored the integration of personalized treatment approaches and precision medicine, allowing for the development of treatment plans tailored to individual patient characteristics and needs. The review emphasizes the potential of ai-assisted image processing in revolutionizing cancer diagnostics. The insights gained from this study contribute to the current understanding of the field and pave the way for future research and development aimed at advancing cancer diagnostics using image analysis and artificial intelligence.

关键词： Cancer Cancer diagnosis Artificial Intelligence Image analysis ai algorithms Clinical practice

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Comparative performance of PD-L1 scoring by pathologists and ai algorithms

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HISTOPATHOLOGY 2025年第0期 2025 Feb 17页

作者： Plass, Markus Olteanu, Gheorghe-Emilian Dacic, Sanja Kern, Izidor Zacharias, Martin Popper, Helmut Fukuoka, Junya Ishijima, Sosuke Kargl, Michaela Murauer, Christoph Kalson, Lipika Brcic, Luka Med Univ Graz Diagnost & Res Inst Pathol Neue Stiftingtalstr 6 A-8010 Graz Austria British Columbia Canc Agcy Dept Pathol Vancouver BC Canada Univ British Columbia Dept Pathol & Lab Med Vancouver BC Canada Univ Pittsburgh Med Ctr Pittsburgh PA USA Univ Clin Resp & Allerg Dis Cytol & Pathol Lab Golnik Slovenia Nagasaki Univ Dept Pathol Informat Grad Sch Biomed Sci Nagasaki Japan Kameda Med Ctr Dept Pathol Kamogawa Japan Yale Sch Med Dept Pathol New Haven CT USA

aim: This study evaluates the comparative effectiveness of pathologists versus artificial intelligence (ai) algorithms in scoring PD-L1 expression in non-small cell lung carcinoma (NSCLC). Immune-checkpoint inhibitors have revolutionized NSCLC treatment, with PD-L1 expression, measured as the tumour proportion score (TPS), serving as a critical predictive biomarker for therapeutic response. Methods and Results: In our analysis, 51 SP263-stained NSCLC cases were scored by six pathologists using light microscopy and whole-slide images (WSI), alongside evaluations by two commercially available software tools: uPath software (Roche) and the PD-L1 Lung Cancer TME application (Visiopharm). The study examined intra- and interobserver agreement among pathologists at TPS cutoffs of 1% and 50%, revealing moderate interobserver agreement (Fleiss' kappa 0.558) for TPS <1% and almost perfect agreement (Fleiss' kappa 0.873) for TPS >= 50%. Intraobserver consistency was high, with Cohen's kappa ranging from 0.726 to 1.0. Comparisons between the ai algorithms and the median pathologist scores showed fair agreement for uPath (Fleiss' kappa 0.354) and substantial agreement for the Visiopharm application (Fleiss' kappa 0.672) at the 50% TPS cutoff. Conclusion: These results indicate that while there is strong interobserver concordance among pathologists at higher TPS levels, the performance of ai algorithms is less consistent. The study underscores the need for further refinement of ai tools to match the reliability of expert human evaluation, particularly in critical clinical decision-making contexts.

关键词： ai algorithms NSCLC pathologists PD-L1 expression tumour proportion score (TPS)

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Windscreen Optical Quality for ai algorithms: Refractive Power and MTF not Sufficient 26

Windscreen Optical Quality for AI Algorithms: Refractive Pow...

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IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

作者： Wolf, Dominik Werner Ulrich, Markus Braun, Alexander Volkswagen Grp Glass Lab Wolfsburg Germany Karlsruhe Inst Technol Inst Photogram & Remote Sens Karlsruhe Germany Univ Appl Sci Duesseldorf Fac Elect Engn & Info Tech Dusseldorf Germany

ISBN: (纸本)9798350399462

Windscreen optical quality is an important aspect of any advanced driver assistance system, and also for future autonomous driving, as today at least some cameras of the sensor suite are situated behind the windscreen. Automotive mass production processes require measurement systems that characterize the optical quality of the windscreens in a meaningful way, which for modern perception stacks implies meaningful for artificial intelligence (ai) algorithms. The measured optical quality needs to be linked to the performance of these algorithms, such that performance limits - and thus production tolerance limits - can be defined. In this article we demonstrate that the main metric established in the industry - refractive power - is fundamentally not capable of capturing relevant optical properties of windscreens. Further, as the industry is moving towards the modulation transfer function (MTF) as an alternative, we mathematically show that this metric cannot be used on windscreens alone, but that the windscreen forms a novel optical system together with the optics of the camera system. Hence, the required goal of a qualification system that is installed at the windscreen supplier and independently measures the optical quality cannot be achieved using MTF. We propose a novel concept to determine the optical quality of windscreens and to use simulation to link this optical quality to the performance of ai algorithms, which can hopefully lead to novel inspection systems.

关键词： windscreen optical quality ai algorithms computer vision refractive power MTF

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Use of ai algorithms in Different Building Typologies for Energy Efficiency towards Smart Buildings

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BUILDINGS 2021年第12期11卷 613页

作者： Bagheri, Ali Genikomsakis, Konstantinos N. Koutra, Sesil Sakellariou, Vasileios Ioakimidis, Christos S. Inteligg PC Karaiskaki 28 Athens 10554 Greece Univ Mons Fac Architecture & Urban Planning Rue Havre 88 B-7000 Mons Belgium Hellen Inst Transport CERTH Ctr Res & Technol Hellas CERTH 6th Km Charilaou Thermi Rd Macedonia 57001 Hellas Greece

Buildings' heating and cooling systems account for an important part of total energy consumption. The EU's directives and engagements motivate building owners and relevant stakeholders in the energy and construction sectors towards net zero energy buildings by maximizing the use of renewable energy sources, ICT, and automation systems. However, the high costs of investment for the renovation of buildings, in situ use of renewable energy production, and installation of expensive ICT infrastructure and automation systems in small-medium range buildings are the main obstacles for the wide adoption of EU building directives in small- and medium-range buildings. On the other hand, the concept of sharing computational and data storage resources among various buildings can be an alternative approach to achieving smart buildings and smart cities where the main control power resides on a server. Unlike other studies that focus on the implementation of ai techniques in a building or separated buildings with local processing resources and data storage, in this work a corporate server was employed to control the heating systems in three building typologies and to examine the potential benefits of controlling existing buildings in a unified energy-savings platform. The key finding of this work is that the ai algorithms incorporated into the proposed system achieved significant energy savings in the order of 20-40% regardless of building typology, building functionality, and type of heating system, despite the COVID-19 measures for frequent ventilation of the buildings, even in cases with older-type heating systems.

关键词： advanced control ai algorithms building modeling computer server energy efficiency in buildings

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Exploring the immune escape mechanisms in gastric cancer patients based on the deep ai algorithms and single-cell sequencing analysis

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JOURNAL OF CELLULAR AND MOLECULAR MEDICINE 2024年第10期28卷 e18379-e18379页

作者： Chen, Wenli Liu, Xiaohui Wang, Houhong Dai, Jingyou Li, Changquan Hao, Yanyan Jiang, Dandan Anhui Med Univ Dept Gen Surg Bozhou Hosp Bozhou Anhui Peoples R China Cent South Univ Xiangya Hosp Dept Nursing Changsha Peoples R China Anhui Med Univ Dept Pediat Surg Bozhou Hosp Bozhou Anhui Peoples R China Anhui Med Univ Dept Articular Surg Bozhou Hosp Bozhou Anhui Peoples R China Univ South China Affiliated Hosp 2 Hengyang Med Sch Dept Emergency 35 Jiefang Ave Hengyang 421001 Hunan Province Peoples R China

Gastric cancer is a prevalent and deadly malignancy, and the response to immunotherapy varies among patients. This study aimed to develop a prognostic model for gastric cancer patients and investigate immune escape mechanisms using deep machine learning and single-cell sequencing analysis. Data from public databases were analysed, and a prediction model was constructed using 101 algorithms. The high-aiDPS group, characterized by increased aiDPS expression, exhibited worse survival, genomic variations and immune cell infiltration. These patients also showed immunotherapy tolerance. Treatment strategies targeting the high-aiDPS group identified three potential drugs. Additionally, distinct cluster groups and upregulated aiDPS-associated genes were observed in gastric adenocarcinoma cell lines. Inhibition of GHRL expression suppressed cancer cell activity, inhibited M2 polarization in macrophages and reduced invasiveness. Overall, aiDPS plays a critical role in gastric cancer prognosis, genomic variations, immune cell infiltration and immunotherapy response, and targeting GHRL expression holds promise for personalized treatment. These findings contribute to improved clinical management in gastric cancer.

关键词： ai algorithms gastric adenocarcinoma immune escape macrophages single-cell analysis

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Pervasive computing in the context of COVID-19 prediction with ai-based algorithms

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INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS 2020年第5期16卷 477-487页

作者： Magesh, S. Niveditha, V. R. Rajakumar, P. S. RamMohan, S. Radha Natrayan, L. Maruthi Technocrat E Serv Chennai Tamil Nadu India Dr MGR Educ & Res Inst Dept Comp Sci & Engn Chennai Tamil Nadu India Dr MGR Educ & Res Inst Dept Comp Applicat Chennai Tamil Nadu India VIT Univ Sch Mech Engn Chennai Campus Chennai Tamil Nadu India

Purpose The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters. Design/methodology/approach For data collection, Infrared Thermometer, Hikvision's Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease. Findings Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history. Originality/value The proposed research collected COVID -19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.

关键词： Prediction Sensors Artificial intelligence Pervasive computing ai algorithms Smart devices

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ai BASED algorithms FOR THE DETECTION OF (IR)REGULARITY IN MUSICAL STRUCTURE

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2020年第4期30卷 761-772页

作者： Mihelac, Lorena Povh, Janez Sch Ctr Novo Mesto SciDrom Sci Lab Segova Ul 112 Novo Mesto 8000 Slovenia Univ Ljubljana Fac Mech Engn Askerceva Ul 6 Ljubljana 1000 Slovenia Inst Math Phys & Mech Jadranska Ul 19 Ljubljana 1000 Slovenia

Regularity in musical structure is experienced as a strongly structured texture with repeated and periodic patterns, with the musical ideas presented in an appreciable shape to the human mind. We recently showed that manipulation of musical content (i.e., deviation of musical structure) affects the perception of music. These deviations were detected by musical experts, and the musical pieces containing them were labelled as irregular. In this study, we replace the human expert involved in detection of (ir)regularity with artificial intelligence algorithms. We evaluated eight variables measuring entropy and information content, which can be analysed for each musical piece using the computational model called Information Dynamics of Music and different viewpoints. The algorithm was tested using 160 musical excerpts. A preliminary statistical analysis indicated that three of the eight variables were significant predictors of regularity (E_cpitch, IC_cpintfref, and E_cpintfref). Additionally, we observed linear separation between regular and irregular excerpts;therefore, we employed support vector machine and artificial neural network (ANN) algorithms with a linear kernel and a linear activation function, respectively, to predict regularity. The final algorithms were capable of predicting regularity with an accuracy ranging from 89% for the ANN algorithm using only the most significant predictor to 100% for the ANN algorithm using all eight prediction variables.

关键词： regularity musical structure perception ai algorithms

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Basic emotion detection accuracy using artificial intelligence approaches in facial emotions recognition system: A systematic review

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APPLIED SOFT COMPUTING 2025年 172卷

作者： Hsu, Chia-Feng Mudiyanselage, Sriyani Padmalatha Konara Agustina, Rismia Lin, Mei-Feng Natl Cheng Kung Univ Coll Med Dept Nursing Tainan Taiwan Lambung Mangkurat Univ Fac Med Sch Nursing Banjarbaru Indonesia Natl Hosp Sri Lanka Operating Theater Dept Colombo Sri Lanka Natl Cheng Kung Univ Inst Behav Med Coll Med Tainan Taiwan 1 Tai Hsueh Rd Tainan 701 Taiwan

Facial emotion recognition (FER) systems are pivotal in advancing human communication by interpreting emotions such as happiness, sadness, anger, fear, surprise, and disgust through artificial intelligence (ai). This systematic review examines the accuracy of detecting basic emotions, evaluates the features, algorithms, and datasets used in FER systems, and proposes a taxonomy for their integration into healthcare. A comprehensive search of six databases, covering publications from January 1990 to March 2023, identified 4073 articles, with 35 studies meeting inclusion criteria. The review revealed that happiness and surprise achieved the highest mean detection accuracies (96.42 % and 96.32 %, respectively), whereas anger and disgust exhibited lower accuracies (91.68 % and 93.71 %, respectively). Fear and sadness had a mean accuracy of 93.87 %. Among ai algorithms, GFFNN demonstrated the highest accuracy (100 %), followed by KNN (97.99 %) and DDBNN (97.77 %). CNN and SVM were the most commonly used algorithms, showing competitive accuracies. The CK+ dataset, while extensively employed, demonstrated a mean accuracy of 96.08 %, lower than RAVDESS, Oulu-CASIA, and other databases.

关键词： Facial emotion recognition Artificial intelligence Basic emotion detection Healthcare applications Datasets ai algorithms

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Exploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis

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JOURNAL OF IMAGING 2025年第1期11卷 6-6页

作者： Stathopoulos, Ioannis Stoklasa, Roman Kouri, Maria Anthi Velonakis, Georgios Karavasilis, Efstratios Efstathopoulos, Efstathios Serio, Luigi Natl & Kapodistrian Univ Athens Attikon Univ Hosp Med Sch Dept Radiol 2 Athens 11527 Greece CERN Technol Dept CH-1211 Geneva Switzerland Masaryk Univ Fac Informat Ctr Biomed Image Anal Brno 60200 Czech Republic Democritus Univ Thrace Sch Med Med Phys Lab Alexandroupolis 68100 Greece

Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of ai algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts' accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH). We present the segmentation results for both the whole abnormal volume and for each abnormal component inside the examinations of the validation set. In the first case, a dice score coefficient (DSC), sensitivity, and precision of 0.76, 0.78, and 0.82, respectively, were found, while in the second case the model detected and segmented correct (True positives) the 48.8% (DSC >= 0.5) of abnormal components, partially correct the 27.1% (0.05 > DSC > 0.5), and missed (False Negatives) the 24.1%, while it produced 25.1% False Positives. Finally, we present an extended analysis between the True positives, False Negatives, and False positives versus their position inside the brain, their intensity at three MRI modalities (FLaiR, T2, and T1ce) and their volume.

关键词： deep learning magnetic resonance imaging (MRI) ai algorithms tumors strokes multiple sclerosis (MS) white matter hyperintensities (WMH) segmentation

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Computer-Driven Assessment of Weighted Attributes for E-Learning Optimization

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COMPUTERS 2025年第4期14卷 116-116页

作者： Ovtsarenko, Olga Safiulina, Elena Vilnius Gediminas Tech Univ Fac Fundamental Sci Sauletekio Al 11 LT-10223 Vilnius Lithuania TTK Univ Appl Sci Ctr Sci Parnu Mnt 62 EE-10135 Tallinn Estonia

Computer-driven assessment has revolutionized the way educational and professional assessments are conducted. Using artificial intelligence for data analytics, computer-based assessment improves efficiency, accuracy, and optimization of learning across disciplines. Optimizing e-learning requires a structured approach to analyzing learners' progress and adjusting instruction accordingly. Although learning effectiveness is influenced by numerous parameters, competency-based assessment provides a structured and measurable way to evaluate learners' achievements. This study explores the application of artificial intelligence algorithms to optimize e-learners' studying within a generalized e-course framework. A competency-based assessment model was developed using weighted parameters derived from Bloom's taxonomy. The key contribution of this work is an innovative method for calculating competency scores using weighted attributes and a dynamic assessment parameter, making the optimization process applicable to both learners and instructors. The results indicate that using the weighted attribute method with a dynamic assessment parameter can improve the structuring of e-courses, increase learner engagement, and provide instructors with a clearer understanding of learners' progress. The proposed approach supports data-driven decision making in e-learning, ensuring a personalized learning experience, and improving overall learning outcomes.

关键词： computer-driven assessment competency-based knowledge weighted attributes e-learning optimization ai algorithms

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