Evaluation of the repeatability of kinesiological measurements of spontaneous infant movements using OSESEC computer analysis algorithms.

Purpose: There is a need to create objective and reproducible tool for assessing the quality of infant movements. It's substantially important to detect movement disorders in infants as early as possible. The study aimed to evaluate the reproducibility of kinesiological measurements of spontaneous movements performed by 51 infants (aged 6 to 15 weeks) recorded three times for two consecutive days using OSESEC computer analysis algorithms by determining numerical values of parameters, i.e., speed, acceleration, direction, and movement trajectory. Methods: The study group consisted of 51 infants. The diagnostic method of Prechtl was used for qualitative assessment. The quantitative assessment was based on the use of a OSESEC system. Numerical values for all movement parameters were determined, and the data obtained in the study were used for statistical analysis. Results: Analysis including movement parameter values on three consecutive recordings for the same infant revealed no statistically significant differences in location ( p = 0.073), range ( p = 0.557), shape ( p = 0.289), mean acceleration ( p = 0.124) and mean speed ( p = 0.767). This confirms the reproducibility of measurements of the proposed parameters of the objectification of spontaneous infant movements. Conclusions: The interpretability and accuracy of the presented parameters were proved. All parameters estimation is fully automated. Further research and testing requires a larger study group to create an objective diagnostic device for infants.

A cohort of Ontario physicians' knowledge regarding autism spectrum disorder: a mixed methods study.

Employing a mixed methods approach, this study examined the knowledge and information Ontario physicians have concerning Autism Spectrum Disorder (ASD). Physicians are at the front line when it comes to identifying ASD. The Healthcare Professional Questionnaire was filled out by 46 physicians and 16 filled out the Knowledge about Childhood Autism among Health Workers Questionnaire (KCAHW) before being asked to participate in a semi-structured interview in which 7 participated. Paediatricians and psychiatrist reported having the highest levels of knowledge. Findings revealed that physicians are lacking in information regarding some early signs (i.e. social smiling = 50%), added diagnostic criteria (i.e. hypo-reactivity to sensory information = 56%), and the prevalence rate of ASD (average estimate of 1 in 1109). Additionally, exposure to individuals with ASD was found to be a facilitator to knowledge acquisition regarding ASD. The physicians recommended a road map method to disseminate information and increase knowledge and awareness. Implications of the findings include what areas of knowledge and information need to be increased, and how knowledge and competency can be increased, to better care for this population.

A window of opportunity for ICU end-of-life care-A retrospective multicenter cohort study.

BACKGROUND: The "window of opportunity" for intensive care staff to deliver end-of-life (EOL) care lies in the timeframe from "documenting the diagnosis of dying" to death. Diagnosing the dying can be a challenging task in the ICU. We aimed to describe the trajectories for dying patients in Danish intensive care units (ICUs) and to examine whether physicians document that patients are dying in time to perform EOL care and, if so, when a window of opportunity for EOL care exists. METHODS: From the Danish Intensive Care Database, we identified patients ≥18 years old admitted to Danish ICUs between January and December 2020 with an ICU stay of >96 h (four days) and who died during the ICU stay or within 7 days after ICU discharge. A chart review was performed on 250 consecutive patients admitted from January 1, 2020, to ICUs in the Central Denmark Region. RESULTS: In most charts (223 [89%]), it was documented that the patient was dying. Of those patients who received mechanical ventilation, 171 (68%) died after abrupt discontinuation of mechanical ventilation, and 63 (25%) died after gradual withdrawal. Patients whose mechanical ventilation was discontinued abruptly died after a median of 1 h (interquartile range [IQR]: 0-15) and 5 h (IQR: 2-15) after a diagnosis of dying was recorded. In contrast, patients with a gradual withdrawal died after a median of 108 h (IQR: 71-189) and 22 h (IQR: 5-67) after a diagnosis of dying was recorded. CONCLUSIONS: EOL care hinges on the ability to diagnose the dying. This study shows that there is a window of opportunity for EOL care, particularly for patients who are weaned from mechanical ventilation. This highlights the importance of intensifying efforts to address EOL care requirements for ICU patients and those discharged from ICUs who are not eligible for readmission.

Toward reliable diabetes prediction: Innovations in data engineering and machine learning applications.

Objective: Diabetes is a metabolic disorder that causes the risk of stroke, heart disease, kidney failure, and other long-term complications because diabetes generates excess sugar in the blood. Machine learning (ML) models can aid in diagnosing diabetes at the primary stage. So, we need an efficient ML model to diagnose diabetes accurately. Methods: In this paper, an effective data preprocessing pipeline has been implemented to process the data and random oversampling to balance the data, handling the imbalance distributions of the observational data more sophisticatedly. We used four different diabetes datasets to conduct our experiments. Several ML algorithms were used to determine the best models to predict diabetes faultlessly. Results: The performance analysis demonstrates that among all ML algorithms, random forest surpasses the current works with an accuracy rate of 86% and 98.48% for Dataset 1 and Dataset 2; extreme gradient boosting and decision tree surpass with an accuracy rate of 99.27% and 100% for Dataset 3 and Dataset 4, respectively. Our proposal can increase accuracy by 12.15% compared to the model without preprocessing. Conclusions: This excellent research finding indicates that the proposed models might be employed to produce more accurate diabetes predictions to supplement current preventative interventions to reduce the incidence of diabetes and its associated costs.

Exome Sequence Analysis to Characterize Undiagnosed Family Segregating Motor Impairment and Dystonia.

Background: Hypermanganesemia with dystonia 1 (HMNDYT1) is a rare genetic disorder characterized by elevated blood manganese levels. This condition is associated with polycythemia, motor neurodegeneration with extrapyramidal features, and hepatic dysfunction, which can progress to cirrhosis in some patients. Materials and Methods: In this study, a consanguineous Saudi family with two affected individuals exhibiting symptoms of severe motor impairment, spastic paraparesis, postural instability, and dystonia was studied. Clinical and radiographic evaluations were conducted on the affected individuals. Whole exome sequencing (WES) was performed to diagnose the disease and to determine the causative variant underlying the phenotype. Moreover, Sanger sequencing was used for validation and segregation analysis of the identified variant. Bioinformatics tools were utilized to predict the pathogenicity of candidate variants based on ACMG criteria. Results: Exome sequencing detected a recurrent homozygous missense variant (c.266T>C; p.L89P) in exon 1 of the SLC30A10 gene. Sanger sequencing was employed to validate the segregation of the discovered variant in all available family members. Bioinformatics tools predicted that the variant is potentially pathogenic. Moreover, conservation analysis showed that the variant is highly conserved in vertebrates. Conclusions: This study shows that exome sequencing is instrumental in diagnosing undiagnosed neurodevelopmental disorders. Moreover, this study expands the mutation spectrum of SLC30A10 in distinct populations.

The Role of Artificial Intelligence in Healthcare: Enhancing Coronary Computed Tomography Angiography for Coronary Artery Disease Management.

This review aims to explore the potential of artificial intelligence (AI) in coronary CT angiography (CCTA), a key tool for diagnosing coronary artery disease (CAD). Because CAD is still a major cause of death worldwide, effective and accurate diagnostic methods are required to identify and manage the condition. CCTA certainly is a noninvasive alternative for diagnosing CAD, but it requires a large amount of data as input. We intend to discuss the idea of incorporating AI into CCTA, which enhances its diagnostic accuracy and operational efficiency. Using such AI technologies as machine learning (ML) and deep learning (DL) tools, CCTA images are automated to perfection and the analysis is significantly refined. It enables the characterization of a plaque, assesses the severity of the stenosis, and makes more accurate risk stratifications than traditional methods, with pinpoint accuracy. Automating routine tasks through AI-driven CCTA will reduce the radiologists' workload considerably, which is a standard benefit of such technologies. More importantly, it would enable radiologists to allocate more time and expertise to complex cases, thereby improving overall patient care. However, the field of AI in CCTA is not without its challenges, which include data protection, algorithm transparency, as well as criteria for standardization encoding. Despite such obstacles, it appears that the integration of AI technology into CCTA in the future holds great promise for keeping CAD itself in check, thereby aiding the fight against this disease and begetting better clinical outcomes and more optimized modes of healthcare. Future research on AI algorithms for CCTA, making ethical use of AI, and thereby overcoming the technical and clinical barriers to widespread adoption of this new tool, will hopefully pave the way for profound AI-driven transformations in healthcare.

Strategies for diagnosing Nosema bombycis (Microsporidia: Nosematidae); the agent of pebrine disease.

Pebrine disease, caused by Nosema bombycis (N. bombycis), is the most important pathogen known to the silk industry. Historical evidence from several countries shows that the outbreaks of pebrine disease have largely caused the decline of the sericulture industry. Prevention is the first line to combat pebrine as a deadly disease in silkworm; however, no effective treatment has yet been presented to treat the disease. Many different methods have been used for detection of pebrine disease agent. This review focuses on the explanation and comparison of these methods, and describes their advantages and/or disadvantages. Also, it highlights the ongoing advances in diagnostic methods for N. bombycis that could enable efforts to halt this microsporidia infection. The detection methods are categorized as microscopic, immunological and nucleic acid-based approaches, each with priorities over the other methods; however, the suitability of each method depends on the available equipment in the laboratory, the mass of infection, and the speed and sensitivity of detection. The accessibility and economic efficiency are compared as well as the speed and the sensitivity for each method. Although, the light microscopy is the most common method for detection of N. bombycis, qPCR is the most preferred method for large data based on speed and sensitivity as well as early detection ability.

Sex Differences in Appendicitis: A Systematic Review.

Appendicitis is one of the most common gastrointestinal conditions a person can develop. Throughout the years of assessing the different focuses of appendicitis, such as origin, symptoms, labs, diagnosis, treatment, and complications, there have been mere mentions of sex differences. One of the most known sex differences in appendicitis is the fact that males are significantly more likely to develop appendicitis compared to females. Another postulated difference is that males may be more likely to develop a perforated appendix. These differences significantly affect the various aspects of diagnosing and treating appendicitis and may even influence the outcome of appendicitis. Sex difference analysis of conditions has been widely researched over the last two decades, and sex can influence and impact conditions from initial presentation to the outcome of treatment. This paper evaluates the sex differences in appendicitis concerning incidence, risk factors, symptoms, diagnosis technique, treatment, and outcomes across ages. Following PRISMA guidelines, this systematic review reviewed PubMed, ScienceDirect, and ProQuest databases for articles pertaining to sex differences in appendicitis. The original article count was 21,121, which was narrowed down to 28 publications. It was found that, as previously described, males had a significantly higher rate of appendicitis, as well as were at significant risk of perforated appendicitis. No official risk factors were found to differ between the sexes, but males were more likely to complain of symptoms like right lower quadrant cramps/tenderness/pain and loss of appetite. Scores such as the pediatric appendicitis score (PAS) and Ohmann have been used to diagnose appendicitis, but the PAS was significantly more accurate for females, and the Ohmann resulted in significantly fewer negative appendectomies in females as well. Ultrasound and computed tomography (CT) are still the gold standards for diagnosis; however, while time to CT was significantly delayed in females, they were more likely to undergo extensive imaging, possibly to rule out other conditions. Males were more likely to undergo open appendectomies compared to females, who more frequently underwent laparoscopic appendectomy, yet females were more likely to experience complications. Further research should evaluate the influences that can predict postoperative outcomes following appendectomies between sexes and how to prevent/reduce their occurrence.

Decision making and management of root caries: A practice-based survey.

OBJECTIVES: With increasing life expectancy and improved preventive measures, teeth are retained longer, leading to a rise in prevalence of root caries lesions (RCL). However, little is known about how dentists manage this condition. The present survey aimed to evaluate the knowledge of Swiss dentists on decision making and management of RCL. METHOD: The survey evaluated dentists' knowledge, clinical routines, and demographics concerning RCL. Dentists were contacted via email and local newsletters, and 383 dentists from 25 (out of 26) cantons responded. Mann-Whitney U test, χ2 test, intraclass correlation coefficients, Spearman correlation and Chi Square were used. RESULTS: The dentists had a mean(SD) working experience of 22.5(12) years. Most dentists correctly classified an inactive (67%) and an active (81%)RCL. Although the inactive lesion did not call for restorative treatments, 61% of the dentist declared they would restore it. From the active lesion,83% would restore it. The invasive treatments leaned toward complete caries excavation with composite resin as preferred restorative material. There were significant correlations between material choice and expected success rates. Among the non-invasive options, oral hygiene instructions and (highly-)fluoridated toothpaste were favored. Most dentists declared having a recall system for such patients, with biannual follow-ups preferred. The dentists' place of education significantly influenced restorative decisions (p < 0.001), while participants' age (≥60years) impacted activity status (p = 0.048) and restorative decisions (p = 0.02). CONCLUSION: Material preferences for non-invasive or invasive management varied greatly and there were minimal differences in the management of inactive or an active RCL. Moreover, diagnosing active lesions appeared easier than diagnosing inactive ones. CLINICAL SIGNIFICANCE: Despite diverse material preferences for (non-)invasive treatments, a strong positive correlation existed between the chosen restorative material and its expected 2-year success rate. Moreover, diagnosing active lesions appeared easier than diagnosing inactive ones. The outcome emphasis the need to align guideline recommendations with their application in private dental practices.

Atypical Presentation of Appendicitis Leading to Exploratory Laparotomy.

Appendicitis typically presents with characteristic symptoms such as right lower quadrant abdominal pain, localizing to McBurney's point, facilitating diagnosis. Here, we report a case of a 19-year-old female who exhibited atypical manifestations, including lower abdominal pain and associated hematochezia. Despite inconclusive findings from imaging modalities and laboratory investigations, persistent pain prompted exploratory laparotomy, revealing appendicitis. This case highlights the diagnostic challenge posed by variant presentations of appendicitis, emphasizing the importance of clinical judgment and vigilance in surgical decision-making.

Shoulder MRI parameters in anticipating anterior shoulder dislocation: Are they a reliable and reproducible tool in clinical practice?

BACKGROUND: Anterior shoulder dislocation (ASD) is a frequently observed musculoskeletal injury that is often encountered in the context of sports activities or as a result of trauma. Several magnetic resonance imaging (MRI) parameters have been previously investigated for the purpose of characterizing the anatomical features, which could potentially be responsible for the episodes of instability. These measurements have the potential to identify patients who are susceptible to dislocation. Consequently, ensuring the reliability and consistency of these measurements is crucial in the diagnosis and the management of athletic or traumatic shoulder injuries. METHODS: A group of four students, who had no previous experience in reading MRI series, were selected to perform radiographic measurements on specific parameters of MRI scans. These parameters were glenoid version, glenoid depth, glenoid width, humeral head diameter, humeral containing angle, and the ratio of humeral head diameter to glenoid diameter. The four participants conducted two distinct readings on a total of 28 sets of shoulder MRI scans. Simultaneously, the aforementioned measures were assessed by a consultant shoulder surgeon. RESULTS: A total of 1512 measurements were categorized into nine sets: eight from students' measurements (two per student) and one from the consultant. Intra-rater reliability assessed by the intra-class correlation (ICC) test indicated excellent or good reliability for all parameters (p < 0.05), with glenoid depth showing the highest (0.925) and humeral-containing angles the lowest (0.675) ICC value. Inter-rater correlation, also evaluated using ICC, demonstrated strong correlation (p < 0.05), with glenoid diameter having the highest ICC score (0.935) and glenoid depth the lowest (0.849). Agreement analysis, expressed by Cohen's Kappa test, revealed substantial agreement (p < 0.05) for all parameters, with humeral head diameter having the highest agreement (0.90) and humeral-containing angle the lowest (0.73). CONCLUSION: In this study, intra- and inter-rater MRI parameters are substantially concordant. Credibility comes from these reliability and agreement analyses' statistical significance. Glenoid diameter and depth are the most reliable intrarater and interrater, respectively. Best agreement was with the humeral-containing angle. These data demonstrate repeatability and clinical relevance. LEVEL OF EVIDENCE: Level IV.

UBE2C: A pan-cancer diagnostic and prognostic biomarker revealed through bioinformatics analysis.

BACKGROUND: The diverse and complex attributes of cancer have made it a daunting challenge to overcome globally and remains to endanger human life. Detection of critical cancer-related gene alterations in solid tumor samples better defines patient diagnosis and prognosis, and indicates what targeted therapies must be administered to improve cancer patients' outcome. MATERIALS AND METHODS: To identify genes that have aberrant expression across different cancer types, differential expressed genes were detected within the TCGA datasets. Subsequently, the DEGs common to all pan cancers were determined. Furthermore, various methods were employed to gain genetic alterations, co-expression genes network and protein-protein interaction (PPI) network, pathway enrichment analysis of common genes. Finally, the gene regulatory network was constructed. RESULTS: Intersectional analysis identified UBE2C as a common DEG between all 28 types of studied cancers. Upregulated UBE2C expression was significantly correlated with OS and DFS of 10 and 9 types of cancer patients. Also, UBE2C can be a diagnostic factor in CESC, CHOL, GBM, and UCS with AUC = 100% and diagnose 19 cancer types with AUC ≥90%. A ceRNA network constructed including UBE2C, 41 TFs, 10 shared miRNAs, and 21 circRNAs and 128 lncRNAs. CONCLUSION: In summary, UBE2C can be a theranostic gene, which may serve as a reliable biomarker in diagnosing cancers, improving treatment responses and increasing the overall survival of cancer patients and can be a promising gene to be target by cancer drugs in the future.

Ultrasound guided compressibility of the lower leg anterior tibial muscle compartment: a feasibility study.

OBJECTIVES: Some young individuals participating in sports activities may encounter lower leg muscle pain and tightness, potentially indicating chronic exertional compartment syndrome (CECS). While muscle pressure measurement is typically recommended for diagnosis, it is invasive and associated with low sensitivity and specificity. Thus, there is a need for novel diagnostic approaches. METHODS: This feasibility study aims to assess whether an ultrasound-guided technique can effectively measure the compressibility of the anterior tibial muscle compartment, focusing on optimal leg positioning and identifying reliable external and internal anatomical landmarks. The compressibility of the anterior tibial muscle compartment was evaluated using ultrasound images obtained at 10 mmHg and 80 mmHg external pressure, with the drop in compartment thickness used to calculate the compressibility ratio. Measurements were conducted in various leg positions and utilizing different external and internal landmarks. RESULTS: Studies in healthy volunteers showed that knee and heel support positioning, measuring at the leg's widest circumference, and using the interosseous membrane as an internal landmark yielded the lowest measurement variability with an intra class correlation of .977 (.764-1.000; 95%-confidence interval). CONCLUSION: These findings suggest that ultrasound-guided techniques can feasibly determine the compressibility ratio of the anterior tibial muscle compartment, providing valuable insights for standardized protocols in future studies on suspected cases of chronic exertional compartment syndrome.

An automatic diagnostic model for the detection and classification of cardiovascular diseases based on swarm intelligence technique.

Globally, cardiovascular diseases (CVDs) rank among the leading causes of mortality. One out of every three deaths is attributed to cardiovascular disease, according to new World Heart Federation research. Cardiovascular disease can be caused by a number of factors, including stress, alcohol, smoking, a poor diet, inactivity, and other medical disorders like high blood pressure or diabetes. In contrast, for the vast majority of heart disorders, early diagnosis of associated ailments results in permanent recovery. Using newly developed data analysis technology, examining a patient's medical record could aid in the early detection of cardiovascular disease. Recent work has employed machine learning algorithms to predict cardiovascular illness on clinical datasets. However, because of their enormous dimension and class imbalance, clinical datasets present serious issues. An inventive model is offered in this work for addressing these problems. An efficient decision support system, also known as an assistive system, is proposed in this paper for the diagnosis and classification of cardiovascular disorders. It makes use of an optimisation technique and a deep learning classifier. The efficacy of traditional techniques for predicting cardiovascular disease using medical data is anticipated to advance with the combination of the two methodologies. Deep learning systems can reduce mortality rates by predicting cardiovascular illness based on clinical data and the patient's severity level. For an adequate sample size of synthesized samples, the optimisation process chooses the right parameters to yield the best prediction from an enhanced classifier. The 99.58% accuracy was obtained by the proposed method. Also, PSNR, sensitivity, specificity, and other metrics were calculated in this work and compared with systems that are currently in use.

Potential merits and flaws of large language models in epilepsy care: A critical review.

The current pace of development and applications of large language models (LLMs) is unprecedented and will impact future medical care significantly. In this critical review, we provide the background to better understand these novel artificial intelligence (AI) models and how LLMs can be of future use in the daily care of people with epilepsy. Considering the importance of clinical history taking in diagnosing and monitoring epilepsy-combined with the established use of electronic health records-a great potential exists to integrate LLMs in epilepsy care. We present the current available LLM studies in epilepsy. Furthermore, we highlight and compare the most commonly used LLMs and elaborate on how these models can be applied in epilepsy. We further discuss important drawbacks and risks of LLMs, and we provide recommendations for overcoming these limitations.

AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis.

(1) Background: Lung cancer's high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI's performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis followed the PRISMA guidelines rigorously, outlining a comprehensive protocol and employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring the selection of high-quality data relevant to AI's role in lung cancer detection. The extraction of key study details and performance metrics, followed by quality assessment, facilitated a robust analysis using R software (Version 4.3.0). The process, depicted via a PRISMA flow diagram, allowed for the meticulous evaluation and synthesis of the findings in this review. (3) Results: From 1024 records, 39 studies met the inclusion criteria, showcasing diverse AI model applications for lung cancer detection, emphasizing varying strengths among the studies. These findings underscore AI's potential for early lung cancer diagnosis but highlight the need for standardization amidst study variations. The results demonstrate promising pooled sensitivity and specificity of 0.87, signifying AI's accuracy in identifying true positives and negatives, despite the observed heterogeneity attributed to diverse study parameters. (4) Conclusions: AI demonstrates promise in early lung cancer detection, showing high accuracy levels in this systematic review. However, study variations underline the need for standardized protocols to fully leverage AI's potential in revolutionizing early diagnosis, ultimately benefiting patients and healthcare professionals. As the field progresses, validated AI models from large-scale perspective studies will greatly benefit clinical practice and patient care in the future.

The Effect of Diagnostic Criteria on Dementia Prevalence - A Population-Based Study From Gothenburg, Sweden.

OBJECTIVES: To examine how the use of different diagnostic criteria (Diagnostic and Statistical Manual of Mental Disorders third revised, fourth, and fifth editions [DSM-III-R, DSM-IV, and DSM-5], and the 10th and 11th editions of the International Classification of Diseases [ICD-10 and ICD-11] influences the reported prevalence of dementia. METHODS: Two cross-sectional population-based studies of systematically selected 85-year-olds in Gothenburg, Sweden, (N = 774), were examined in comprehensive health examinations including comprehensive neurocognitive examinations. Five algorithms based on the diagnostic criteria in the DSM-III-R, DSM-IV, DSM-5, ICD-10, and ICD-11 were created, including 105 different variables that were operationalized in different ways to match the criteria of each classification system. RESULTS: ICD-11 yielded the highest prevalence of dementia (36.4%), followed by DSM-5 (32.9%), DSM-IV (30.7%), the clinical consensus DSM-III-R diagnosis (26.7%), DSM-III-R (21.4%), and ICD-10 (20.5%). The agreement between the DSM-5 and the ICD-11 was κ = 0.9. All other kappa values ranged between 0.6 and 0.9. CONCLUSIONS: The choice of diagnostic criteria has a large effect on the estimated prevalence of dementia. We found that the recent editions, the DSM-5 and ICD-11, gave a higher prevalence of dementia than older editions. We also show that the attempts to harmonize DSM and ICD have in part been successful, however, there are still differences between the systems.

A computational approach for analysis of intratumoral heterogeneity and standardized uptake value in PET/CT images1.

BACKGROUND: By providing both functional and anatomical information from a single scan, digital imaging technologies like PET/CT and PET/MRI hybrids are gaining popularity in medical imaging industry. In clinical practice, the median value (SUVmed) receives less attention owing to disagreements surrounding what defines a lesion, but the SUVmax value, which is a semi-quantitative statistic used to analyse PET and PET/CT images, is commonly used to evaluate lesions. OBJECTIVE: This study aims to build an image processing technique with the purpose of automatically detecting and isolating lesions in PET/CT images, as well as measuring and assessing the SUVmed. METHODS: The pictures are separated into their respective lesions using mathematical morphology and the crescent region, which are both part of the image processing method. In this research, a total of 18 different pictures of lesions were evaluated. RESULTS: The findings of the study reveal that the threshold is satisfied by both the SUVmax and the SUVmed for most of the lesion types. However, in six instances, the SUVmax and SUVmed values are found to be in different courts. CONCLUSION: The new information revealed by this study needs to be further investigated to determine if it has any practical value in diagnosing and monitoring lesions. However, results of this study suggest that SUVmed should receive more attention in the evaluation of lesions in PET and CT images.