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1 A New Approach to Detect Proximal Caries Using Electrical Dental Parameters https://brieflands.com/journals/tatm/articles/170005 10.69107/tatm-170005 1 Background :Proximal caries remains difficult to detect during routine clinical examinations because of their anatomical location beneath contact areas. Objectives :This in vivo study aimed to evaluate electrical dental parameters, specifically electrical resistance and capacitance, at predefined anatomical locations and to assess their diagnostic performance for proximal caries detection. Methods :In this diagnostic observational study, 43 patients presenting with proximal caries in first molars were enrolled. Electrical resistance and capacitance were measured using a benchtop digital multimeter and a digital LC meter at four predefined tooth surfaces. Clinical and radiographic examinations served as reference standards for caries diagnosis. Diagnostic accuracy, sensitivity, and specificity of the electrical measurements were calculated and analyzed. Data were analyzed using the independent samples t-test in SPSS software, version 20.0. Results :Electrical resistance showed a significant reduction in teeth affected by proximal caries compared with intact teeth, particularly at the bucco-lingual surface and mesial-distal occlusal regions (P < 0.05). The highest diagnostic performance was achieved at the bucco-lingual surface, with an accuracy of 89.5%, sensitivity of 86.0%, and specificity of 90.7%. Electrical capacitance did not demonstrate a significant association with proximal caries at any measurement site. Conclusions :Electrical resistance measurements obtained under in vivo conditions can reliably differentiate proximal caries from intact teeth. Measurement at the bucco-lingual surface provides the highest diagnostic accuracy. Electrical resistance may serve as a non-invasive, radiation-free adjunctive diagnostic tool for proximal caries detection in clinical practice. Clinical Relevance :Electrical resistance assessment offers a practical and safe adjunct to conventional diagnostic methods for proximal caries, potentially improving early detection while reducing reliance on radiographic imaging. 1 7 Mohammad Amin Younessi Heravi Department of Medical Physics and Radiology, School of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran a.younessi7@gmail.com Milad Shakeri Student Research Committee, Dental School, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran shakerim@gmail.com Roya Amiri Department of Restorative Dentistry, Dental School, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran roya.amiridaluyi@gmail.com Vahideh Motamedosanaye Department of Restorative Dentistry, Dental School, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran motamedv871@yahoo.com No Keyword 1.pdf [0]Twetman, Axelsson, Dahlén, Espelid, Mejàre, Norlund, et al.Adjunct methods for caries detection: A systematic review of literature. Acta Odontologica Scandinavica. 2013;71(3-4):388-97. [PubMed ID: 22630355]. doi: 10.3109/00016357.2012.690448.##[1]Pretty.Caries detection and diagnosis. Comprehensive Preventive Dentistry. 2012:25-42. doi: 10.1002/9781118703762.ch2.##[2]Ritter, Ramos, Astorga, Shugars, Bader.Visual-tactile versus radiographic caries detection agreement in caries-active adults. Journal of Public Health Dentistry. 2013. [PubMed ID: 23772747]. doi: 10.1111/jphd.12024.##[3]Marotti, Heger, Tinschert, Tortamano, Chuembou, Radermacher, et al.Recent advances of ultrasound imaging in dentistry-a review of the literature. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology. 2013;115(6):819-32. [PubMed ID: 23706922]. doi: 10.1016/j.oooo.2013.03.012.##[4]Bozkurt, Tağtekin, Yanikoglu, Fontana, Gonzalez-Cabezas, Stookey, et al.Capability of an Ultrasonic System to Detect Very Early Caries Lesions on Human Enamel. Marmara Dental Journal. 2013;1(1):16-9. doi: 10.12990/MDJ2013121.##[5]Matalon, Feuerstein, Calderon, Mittleman, Kaffe.Detection of cavitated carious lesions in approximal tooth surfaces by ultrasonic caries detector. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 2007;103(1):109-13. [PubMed ID: 17178503]. doi: 10.1016/j.tripleo.2006.07.023.##[6]Teo, Ashley, Louca.An in vivo and in vitro investigation of the use of ICDAS, DIAGNOdent pen and CarieScan PRO for the detection and assessment of occlusal caries in primary molar teeth. Clinical Oral Investigations. 2013:1-8. [PubMed ID: 23793456]. doi: 10.1007/s00784-013-1021-4.##[7]Çınar, Atabek, Odabaş, Ölmez.Comparison of laser fluorescence devices for detection of caries in primary teeth. International Dental Journal. 2013. [PubMed ID: 23550523]. [PubMed Central ID: PMC9374934]. doi: 10.1111/idj.12024.##[8]Schwass, Leichter, Purton, Swain.Evaluating the efficiency of caries removal using an Er:YAG laser driven by fluorescence feedback control. Archives of Oral Biology. 2012. [PubMed ID: 23123070]. doi: 10.1016/j.archoralbio.2012.09.017.##[9]DeBenedetto, Morais, Novaes, de Almeida Rodrigues, Braga, Mendes, et al.Comparing the reliability of a new fluorescence camera with conventional laser fluorescence devices in detecting caries lesions in occlusal and smooth surfaces of primary teeth. Lasers in Medical Science. 2011;26(2):157-62. [PubMed ID: 20157753]. doi: 10.1007/s10103-010-0757-1.##[10]Bader, Shugars, Bonito.Systematic reviews of selected dental caries diagnostic and management methods. Journal of Dental Education. 2001;65(10):960-8. doi: 10.1002/j.0022-0337.2001.65.10.tb03470.x.##[11]Verdonschot, Rondel, MCDNJM.Validity of electrical conductance measurements in evaluating the marginal integrity of sealant restorations. Caries Research. 1995;29(2):100-6. [PubMed ID: 7728822]. doi: 10.1159/000262049.##[12]Huysmans, Longbottom, Pitts, Los, Bruce.Impedance Spectroscopy of Teeth with and without Approximal Caries Lesions-an in vitro Study. Journal of Dental Research. 1996;75(11):1871-8. [PubMed ID: 9003234]. doi: 10.1177/00220345960750110901.##[13]Hope, Griffiths, Prior.Finding an Alternative to Formalin for Sterilization of Extracted Teeth for Teaching Purposes. Journal of Dental Education. 2013;77(1):68-71. doi: 10.1002/j.0022-0337.2013.77.1.tb05445.x.##[14]Pretty.Caries detection and diagnosis: novel technologies. Journal of Dentistry. 2006;34(10):727-39. [PubMed ID: 16901606]. doi: 10.1016/j.jdent.2006.06.001.##[15]Ng, Ferguson, Payne, Slater.Ultrasonic studies of unblemished and artificially demineralized enamel in extracted human teeth: a new method for detecting early caries. Journal of Dentistry. 1988;16(5):201-9. [PubMed ID: 3063732]. doi: 10.1016/0300-5712(88)90070-X.##[16]Lussi, Hack, Hug, Heckenberger, Megert, Stich, et al.Detection of approximal caries with a new laser fluorescence device. Caries Research. 2006;40(2):97-103. [PubMed ID: 16508265]. doi: 10.1159/000091054.##[17]Penta, Pirvu, Demetrescu.Electrochemical Impedance Spectroscopy (EIS) Investigation on Dental Hard Tissue Whitening Process Using Fluoride and Non-fluoride Carbamide Peroxide Gels. APCBEE Procedia. 2013;7:67-72. doi: 10.1016/j.apcbee.2013.08.014.##

1 Comparison of the Effects of Video-Based and Audio-Based Self-Care Education on Health-Related Quality of Life and Satisfaction in Hemodialysis Patients: A Quasi-Experimental Study https://brieflands.com/journals/tatm/articles/170332 10.69107/tatm-170332 1 Background :Self-care education plays a pivotal role in managing complications and improving treatment outcomes in patients undergoing hemodialysis. Objectives :This study aimed to compare the effects of two remote educational modalities, video-based and audio-based instruction, on health-related quality of life (HRQoL) and treatment satisfaction among patients undergoing hemodialysis. Given the paucity of comparative studies on remote educational modalities in this population, this study addresses an important gap in evidence-based patient education. Methods :This quasi-experimental study was conducted in 2021. Ninety hemodialysis patients from three medical centers in Mashhad, Iran (Montasariyeh Hospital, Kidney Patients Association, and 17-Shahrivar Hospital) were recruited using convenience sampling and assigned to two intervention groups: video-based education (n = 45) and audio-based education (n = 45). The intervention comprised a 4-week self-care educational program delivered in 8 sessions. Data were collected using the Kidney Disease Quality of Life Short Form, version 1.3 (KDQOL-SF™ 1.3), and a researcher-developed satisfaction questionnaire. Statistical analyses were performed using SPSS version 19 and included descriptive and inferential statistics, paired t-tests, independent t-tests, Mann-Whitney U tests, and Wilcoxon tests. Results :Before the intervention, no significant difference was observed between the two groups in total HRQoL scores (P = 0.666). After the intervention, mean HRQoL scores increased significantly in both the video-based and audio-based groups (P = 0.001). However, no statistically significant difference was observed between the two groups in mean post-intervention HRQoL scores (P = 0.634). Notably, the mean satisfaction score was significantly higher in the video-based group than in the audio-based group (P < 0.001). Conclusions :Both educational modalities effectively improved the quality of life of patients undergoing hemodialysis. However, video-based instruction was more acceptable and may be a more effective tool for patient empowerment, as it generated higher satisfaction levels. Therefore, integrating visual educational aids into routine care programs in dialysis units is recommended. 1 7 Somayeh Javan School of Allied Medical Sciences, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran s.javan@nkums.ac.ir Mohaddeseh Mohsenpour School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran Iran mohsenpourmh@mums.ac.ir Fatemeh Araghi School of Allied Medical Sciences, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran araghif_69@yahoo.com Mahshad Langari School of Allied Medical Sciences, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran m.langari@nkums.ac.ir No Keyword 2.pdf [0]Rademan H, Ebrahim Z, Esau N.The development and testing of an educational video for patients with end stage kidney disease receiving dialysis in two tertiary hospitals in Cape Town. Journal of Renal Nutrition. 2025;35(3):425-432. [PubMed ID: 39793678]. doi: 10.1053/j.jrn.2024.12.008.##[1]Yousefi M, Rezaei S, Hajebrahimi S, Falsafi N, Keshvari-Shad F.Peritoneal dialysis vs. hemodialysis among patients with end-stage renal disease in Iran: which is more cost-effective? BMC Nephrology. 2024;25(1):85.##[2]Khajevand Ahmadi M, Abbasi M, Moradinazar M, Ahmadi Jouybari T, Omrani H, Yari Bajelani B, et al.Impact of medical factors on mortality in patients with end-stage renal disease in the west of Iran: A prospective study. Epidemiology and Health System Journal. 2024;11(2):68-73. doi: 10.34172/ehsj.26146.##[3]Lv JC, Zhang LX.Prevalence and disease burden of chronic kidney disease. Renal Fibrosis: Mechanisms and Therapies. 2019;1165:3-15. [PubMed ID: 31399958]. doi: 10.1007/978-981-13-8871-2_1.##[4]Maslakpak MH, Shams S.A comparison of face to face and video-based self care education on quality of life of hemodialysis patients. International Journal of Community Based Nursing and Midwifery. 2015;3(3):234.##[5]Abbasi Abianeh N, Abdollah Zargar S, Amirkhani A, Adelipouramlash A.The effect of self-care education through teach back method on the quality of life in hemodialysis patients. Nephrologie & Therapeutique. 2020;16(4):197-200. doi: 10.1016/j.nephro.2020.01.002.##[6]Bouya S, Balouchi A, Rafiemanesh H, Hesaraki M.Prevalence of chronic kidney disease in Iranian general population: A meta-analysis and systematic review. Therapeutic Apheresis and Dialysis. 2018;22(6):594-9. [PubMed ID: 29974630]. doi: 10.1111/1744-9987.12716.##[7]Chen MF, Chang RE, Tsai HB, Hou YH.Effects of perceived autonomy support and basic need satisfaction on quality of life in hemodialysis patients. Quality of Life Research. 2018;27(3):765-73. [PubMed ID: 29027069]. doi: 10.1007/s11136-017-1714-2.##[8]Ghiasi B, Sarokhani D, Dehkordi AH, Sayehmiri K, Heidari MH.Quality of Life of patients with chronic kidney disease in Iran: Systematic Review and Meta-analysis. Indian Journal of Palliative Care. 2018;24(1):104-111. [PubMed ID: 29440817]. [PubMed Central ID: PMC5801615]. doi: 10.4103/IJPC.IJPC_146_17.##[9]Ahmed YA, Naeem I, Bhatti SJ, Shah BUD, Zafar AA, Ahmed A, et al.Assessment of quality of life in patients with end-stage kidney disease on maintenance hemodialysis using the Missoula-Vitas Quality of Life Index. Cureus. 2024;16(7):e65459. [PubMed ID: 39184788]. [PubMed Central ID: PMC11345103]. doi: 10.7759/cureus.65459.##[10]Gerasimoula K, Lefkothea L, Maria L, Victoria A, Paraskevi T, Maria P.Quality of life in hemodialysis patients. Materia Socio-Medica. 2015;27(5):305-9. [PubMed ID: 26622195]. [PubMed Central ID: PMC4639348]. doi: 10.5455/msm.2015.27.305-309.##[11]Shirazian S, Smaldone AM, Jacobson AM, Fazzari MJ, Weinger K.Improving quality of life and self-care for patients on hemodialysis using cognitive behavioral strategies: A randomized controlled pilot trial. PLoS One. 2023;18(5). e0285156. [PubMed ID: 37141225]. [PubMed Central ID: PMC10159130]. doi: 10.1371/journal.pone.0285156.##[12]Kuwabara A, Su S, Krauss J.Utilizing digital health technologies for patient education in lifestyle medicine. American Journal of Lifestyle Medicine. 2020;14(2):137-42. [PubMed ID: 32231478]. [PubMed Central ID: PMC7092400]. doi: 10.1177/1559827619892547.##[13]Navarro O, Escrivá M, Faubel R, Traver V.Empowering patients living with chronic conditions using video as an educational tool: Scoping review. Journal of Medical Internet Research. 2021;23(7). e26427. doi: 10.2196/26427.##[14]Grimaldi MRM, Guimarães FJ.Health education technologies for people with visual impairment: Integrative review. Texto & Contexto-Enfermagem. 2022;31. e20210236.##[15]Moonaghi HK, Hasanzadeh F, Shamsoddini S, Emamimoghadam Z, Ebrahimzadeh S.A comparison of face to face and video-based education on attitude related to diet and fluids: Adherence in hemodialysis patients. Iranian Journal of Nursing and Midwifery Research. 2012;17(5):360-4.##[16]Narimani K.A study of the effect of self-care training on the hemodialysis patients' quality of life. Daneshvar Medicine. 2009;16(79):63-70.##[17]Taşkin Duman H, Karadakovan A.The effect of video training on symptom burden, comfort level, and quality of life in hemodialysis patients: Clustered randomized controlled trial. Patient Education and Counseling. 2024;126. 108314. doi: 10.1016/j.pec.2024.108314.##[18]Yasari F, Taherian M, Akbarian M, Vasheghani M.The effect of an educational video about healthy diet on metabolic control of patients on hemodialysis: An interventional study with a one-year follow-up. BMC Nephrology. 2024;25(1). 262. [PubMed ID: 39143571]. [PubMed Central ID: PMC11323627]. doi: 10.1186/s12882-024-03693-w.##[19]Ibrahim MH, Ali AM, Allawy MEA.Effect of educational nursing guidelines on self-management and health-related quality of life for hemodialysis patients. Iranian Journal of Nursing and Midwifery Research. 2024;29(4):460-5. [PubMed ID: 39205834]. [PubMed Central ID: PMC11349158]. doi: 10.4103/ijnmr.ijnmr_191_22.##[20]Torabikhah M, Farsi Z, Sajadi SA.Comparing the effects of mHealth app use and face-to-face training on the clinical and laboratory parameters of dietary and fluid intake adherence in hemodialysis patients: A randomized clinical trial. BMC Nephrology. 2023;24(1). 194. [PubMed ID: 37386428]. [PubMed Central ID: PMC10308810]. doi: 10.1186/s12882-023-03246-7.##[21]Ren Q, Shi S, Yan C, Liu Y, Han W, Lin M, et al.Self-management micro-video health education program for hemodialysis patients. Clinical Nursing Research. 2022;31(6):1148-57. [PubMed ID: 34282644]. doi: 10.1177/10547738211033922.##[22]Sarmadi S, Sanaie N, Zare-Kaseb A.Comparing the effects of interactive and conventional video education on activation, treatment adherence, and weight changes in dialysis patients: A randomized clinical trial protocol. PLoS One. 2025;20(10). e0334498. [PubMed ID: 41091721]. [PubMed Central ID: PMC12527215]. doi: 10.1371/journal.pone.0334498.##[23]Tarverdizade Asl P, Lakdizaji S, Ghahramanian A, Seyedrasooli A, Ghavipanjeh Rezaiy S.Effectiveness of text messaging and face to face training on improving knowledge and quality of life of patients undergoing hemodialysis: A randomized clinical trial. Journal of Caring Sciences. 2018;7(2):95-100. [PubMed ID: 29977880]. [PubMed Central ID: PMC6029648]. doi: 10.15171/jcs.2018.015.##[24]Liu Y, Luo X, Ru X, Wen C, Ding N, Zhang J.Impact of a multimodal health education combined with teach-back method on self-management in hemodialysis patients: A randomized controlled trial. Medicine. 2024;103(52). e39971. [PubMed ID: 39969380]. [PubMed Central ID: PMC11688015]. doi: 10.1097/md.0000000000039971.##

1 A Hybrid Approach for Brain Tumor Segmentation Using Fuzzy C-Means and the Imperialist Competitive Algorithm https://brieflands.com/journals/tatm/articles/170560 10.69107/tatm-170560 1 Background :Accurate, automated segmentation of brain tumor regions from magnetic resonance imaging (MRI) is critical for computer-aided diagnosis and radiotherapy planning. Among unsupervised techniques, Fuzzy C-Means (FCM) is widely used because it can accommodate data uncertainty and partial volume effects. However, standard FCM is highly sensitive to the random initialization of cluster centers, which can lead to convergence to local optima and reduced segmentation reproducibility. Objectives :This study aimed to develop a robust hybrid segmentation framework to mitigate the initialization sensitivity of conventional clustering. The primary objective was to leverage the global optimization capability of the Imperialist Competitive Algorithm (ICA) to identify optimal initial cluster centers, thereby ensuring stable convergence and improving segmentation accuracy. Methods :The proposed method integrates ICA with FCM, thereby replacing the conventional random initialization strategy. ICA mimics sociopolitical competition to explore the search space globally and identify near-optimal cluster centroids, which are then used as initial inputs for the subsequent local FCM refinement phase. The framework was evaluated using the BraTS 2024 dataset. Performance was validated against K-means, standard FCM, and Kernel-based Intuitive Fuzzy C-Means (KIFCM) using accuracy, sensitivity, and precision. Results :The experimental analysis showed that the proposed FCM-ICA framework significantly outperformed the comparative algorithms. With an optimal population size of 50 countries, the method achieved a classification accuracy of 88.1% and a sensitivity of 85.5%. In comparison, K-means, standard FCM, and KIFCM yielded accuracies of 66.3%, 82.0%, and 86.9%, respectively. ICA-based initialization effectively mitigated the local optimum problem and provided greater stability than random initialization. Conclusions :Integrating ICA to optimize cluster centers significantly improves fuzzy clustering performance in medical imaging. The proposed hybrid method provides a robust, accurate, and stable solution for brain tumor segmentation and demonstrates strong potential for integration into automated clinical diagnostic workflows. 1 9 Jafar Emamipour Department of Computer and IT Engineering, Payame Noor University, Tehran, Iran Iran j.emamipour@pnu.ac.ir Hamzeh Vahidifar School of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran h.vahidifar@nkums.ac.ir Hossein Nahid-Titkanlue Department of Industrial Engineering, Payame Noor University, Tehran, Iran Iran hossein_nahid@pnu.ac.ir No Keyword 3.pdf [0]Vankdothu R, Hameed MA.Brain tumor segmentation of MR images using SVM and fuzzy classifier in machine learning. Measurement: Sensors. 2022;24. 100440. doi: 10.1016/j.measen.2022.100440.##[1]Ananthi VP, Balasubramaniam P, Kalaiselvi T.A new fuzzy clustering algorithm for the segmentation of brain tumor. Soft Comput. 2016;20(12):4859-4879. doi: 10.1007/s00500-015-1775-5.##[2]Alagarsamy S, Govindaraj V, A S.Automated Brain Tumor Segmentation for MR Brain Images Using Artificial Bee Colony Combined With Interval Type-II Fuzzy Technique. IEEE Trans Ind Inform. 2023;19(11):11150-11159. doi: 10.1109/TII.2023.3244344.##[3]Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, et al.Brain tumor segmentation with deep neural networks. arXiv preprint arXiv:1605. 2695. 2016;35:18-31. [PubMed ID: 27310171]. [PubMed Central ID: PMC12949315]. doi: 10.1016/j.media.2016.05.004.##[4]Ilhan U, Ilhan A.Brain tumor segmentation based on a new threshold approach. Procedia Comput Sci. 2017;120:580-587. doi: 10.1016/j.procs.2017.11.282.##[5]Sert E, Özyurt F, Doğantekin A.A new approach for brain tumor diagnosis system: single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network. Med Hypotheses. 2019;133. 109413. [PubMed ID: 31586812]. doi: 10.1016/j.mehy.2019.109413.##[6]Özyurt F, Sert E, Avcı D.An expert system for brain tumor detection: fuzzy C-means with super resolution and convolutional neural network with extreme learning machine. Med Hypotheses. 2020;134. 109433. [PubMed ID: 31634769]. doi: 10.1016/j.mehy.2019.109433.##[7]Kalantari R, Moqadam R, Loghmani N, Allahverdy A, Shiran MB, Zare-Sadeghi A.Brain tumor segmentation using hierarchical combination of fuzzy logic and cellular automata. J Med Signals Sens. 2022;12(4):263-268. [PubMed ID: 36120403]. [PubMed Central ID: PMC9480508]. doi: 10.4103/jmss.jmss_128_21.##[8]Özyurt F, Sert E, Avci E, Dogantekin E.Brain tumor detection based on convolutional neural network with neutrosophic expert maximum fuzzy-sure entropy. Measurement. 2019;147. 106830. doi: 10.1016/j.measurement.2019.07.058.##[9]Chahal PK, Pandey S.A hybrid weighted fuzzy approach for brain tumor segmentation using MR images. Neural Comput Appl. 2023;35(33):23877-23891. doi: 10.1007/s00521-021-06010-w.##[10]Pereira S, Pinto A, Alves V, Silva CA.Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging. 2016;35(5):1240-1251. [PubMed ID: 26960222]. [PubMed Central ID: PMC12832372]. doi: 10.1109/TMI.2016.2538465.##[11]Wulandari A, Sigit R, Bachtiar MM.Brain Tumor Segmentation to Calculate Percentage Tumor Using MR. Int J Comput Sci. 2017;14:10-20.##[12]Rajan PG, Sundar C.Brain Tumor Detection and Segmentation by Intensity Adjustment. J Comput Sci. 2018;27:115-123.##[13]Sudharani K, Sarma TC, Prasad KS.Advanced Morphological Technique for Automatic Brain Tumor Detection and Evaluation of Statistical Parameters. Procedia Technol. 2016;24:1374-1381. doi: 10.1016/j.protcy.2016.05.153.##[14]Virupakshappa, Amarapur B.Cognition-based MRI brain tumor segmentation technique using modified level set method. Comput Med Imaging Graph. 2019;73(3):35-47. doi: 10.1007/s10111-018-0472-4.##[15]Aslam A, Khan E, Be MMS.Improved Edge Detection Algorithm for Brain Tumor Segmentation. J Digit Imaging. 2019;32:789-798.##[16]Alagarsamy S, Kamatchi K, Govindaraj V, Zhang YD, Thiyagarajan A.Multi-channeled MR brain image segmentation: A new automated approach combining BAT and clustering technique for better identification of heterogeneous tumors. Comput Methods Programs Biomed. 2020;195:105585.##[17]Rajan PG, Sundar C.Intelligent Multigrade Brain Tumor Identification in MRI: A Metaheuristic-Based Uncertain Set Framework. Comput Biol Med. 2021;134:104543.##[18]Ren T, Wang H, Feng H, Xu C, Liu G, Ding P.Study on the improved fuzzy clustering algorithm and its application in brain image segmentation. Biomed Signal Process Control. 2020;57:101796.##[19]Jemimma TA, Vetharaj YJ.Fractional probabilistic fuzzy clustering and optimization based brain tumor segmentation and classification. Multimedia Tools Appl. 2022;81(13):17889-17918. doi: 10.1007/s11042-022-11969-2.##[20]Correia de Verdier M, et al.The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI. arXiv preprint arXiv:2405. 18368. 2024.##

1 Automated Staging of Knee Osteoarthritis Using Radiographic Image Features and SVM Algorithm: A Clinical Decision Support Approach https://brieflands.com/journals/tatm/articles/166240 10.69107/tatm-166240 1 Background :Knee osteoarthritis is one of the most prevalent joint disorders in the elderly, and accurate classification of its severity plays a critical role in therapeutic decision-making. Objectives :This study aimed to develop an automated classification model for assessing knee osteoarthritis severity using radiographic images and clinical features, based on the Support Vector Machine (SVM) algorithm. Methods :In this applied, retrospective, and observational research, 44 radiographic images of the left knee from patients aged 39 to 72 were collected from the radiology department of Imam Ali Hospital in Bojnourd. Four key clinical features — namely, the angle between the femoral and tibial axes, the joint space width (JSW) ratio, the extent of subarticular erosion, and osteophyte structure — were extracted from the images. All features were normalized and evaluated using SVM models with both linear and nonlinear kernels. Model performance was assessed using k-fold cross-validation and analyzed through classification accuracy, sensitivity, and specificity. Osteoarthritis severity was determined using the Kellgren-Lawrence (KL) grading system, as assessed by an orthopedic specialist. Results :The classification accuracy using all features and the radial kernel reached 79.89%. With the radial basis function (RBF) kernel at σ = 0.85, the highest accuracy of 83.53% was achieved. The femur-tibia angle feature alone yielded a reasonably high performance [74.14% with the multilayer perceptron (MLP)], while the osteophyte feature resulted in the lowest classification accuracy (59.22%). Comparative chart analyses revealed that nonlinear kernels had superior discriminatory power compared to linear kernels. Conclusions :The proposed SVM-based model, utilizing interpretable structural features, successfully classified the severity of knee osteoarthritis with acceptable accuracy. The achieved classification accuracy (~84%) suggests potential clinical utility, although direct comparison with human expert performance was not conducted. This approach is recommended as a diagnostic support system, particularly in resource-limited clinical settings. Future research can enhance the model's generalizability and accuracy by incorporating additional clinical data and multi-source imaging modalities. 1 9 Mohammad Amin Younessi Heravi Department of Medical Physics and Radiology, Faculty of Allied Medical Sciences, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran a.younessi7@gmail.com Mohammad Mahdi Khalilzadeh Biomedical Engineering Group, Islamic Azad University, Mashhad, Iran Iran mmkhalilzadeh@gmail.com Ghorbanali Mohammadzadeh Imam Ali Hospital, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran butorabali@gmail.com Hamid-Reza Sadoughi Department of Medical Physics and Radiology, Faculty of Allied Medical Sciences, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran sadoughi.hamid@gmail.com No Keyword 4.pdf [0]Tiulpin A, Saarakkala S.Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs Using Deep Convolutional Neural Networks. Diagnostics. 2020;10(11). [PubMed ID: 33182830]. [PubMed Central ID: PMC7697270]. doi: 10.3390/diagnostics10110932.##[1]Yoon JS, Yon CJ, Lee D, Lee JJ, Kang CH, Kang SB, et al.Assessment of a novel deep learning-based software developed for automatic feature extraction and grading of radiographic knee osteoarthritis. BMC Musculoskelet Disord. 2023;24(1):869. [PubMed ID: 37940935]. [PubMed Central ID: PMC10631128]. doi: 10.1186/s12891-023-06951-4.##[2]Stachowiak GW, Wolski M, Woloszynski T, Podsiadlo P.Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis. Biosurface Biotribol. 2016;2(4):162-72. doi: 10.1016/j.bsbt.2016.11.004.##[3]Khalid A, Senan EM, Al-Wagih K, Ali Al-Azzam MM, Alkhraisha ZM.Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted. Diagnostics. 2023;13(9). [PubMed ID: 37175000]. 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1 Leveraging Advanced AI Technologies for Radiotherapy Dose Calculation: A Narrative Review https://brieflands.com/journals/tatm/articles/167315 10.69107/tatm-167315 1 Context :Accurate radiotherapy dose calculation is critical for optimizing treatment efficacy and minimizing toxicity. Traditional algorithms, while clinically validated, often struggle with complex anatomical variations and heterogeneous tissue compositions. Recent advances in artificial intelligence (AI) offer promising alternatives for enhancing dose prediction accuracy and workflow efficiency. Objectives :This review aims to critically appraise the current landscape of AI-based radiotherapy dose calculation methods, comparing their performance, interpretability, and clinical applicability across various algorithmic families. Methods :A comprehensive literature search was conducted using PubMed, Scopus, and IEEE Xplore databases, focusing on studies published between 2015 and 2025. Included articles were categorized into six AI domains: Machine learning (ML), deep learning (DL), reinforcement learning (RL), Bayesian models, fuzzy logic systems, and evolutionary algorithms. Comparative analysis was performed based on dosimetric accuracy, computational efficiency, explainability, and integration with treatment planning systems (TPS). Results :The DL models, particularly convolutional neural networks (CNNs) and transformer-based architectures, demonstrated superior performance in dose prediction for head and neck, prostate, and lung cancers. The RL approaches showed potential in adaptive planning scenarios, while Bayesian and fuzzy logic models offered enhanced interpretability. Evolutionary algorithms were effective in multi-objective optimization but required extensive computational resources. Despite promising results, most studies lacked external validation and standardized benchmarking. Conclusions :The AI-driven dose calculation methods represent a transformative shift in radiotherapy planning. However, challenges remain in clinical translation, including algorithm transparency, regulatory approval, and integration with existing workflows. Future research should prioritize multi-institutional validation, hybrid model development, and human-AI collaboration frameworks to ensure safe and effective deployment. 1 8 Hamid-Reza Sadoughi Department of Medical Physics and Radiology, Faculty of Allied Medical Sciences, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran sadoughi.hamid@gmail.com Azam Orooji Department of Advanced Technologies, School of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran Iran orooji_9898@yahoo.com No Keyword 5.pdf [0]Dudhe SS, Mishra G, Parihar P, Nimodia D, Kumari A.Radiation Dose Optimization in Radiology: A Comprehensive Review of Safeguarding Patients and Preserving Image Fidelity. 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