Fahmida Haque

Background/Objectives: MRI is a non-invasive tool which can be used to assess baseline gonadal anatomy, including changes during puberty. Volumetric characterization offers valuable insights about the reproductive system and gonads, but annotation is cumbersome, and no AI tool is currently available. This study aimed to develop two open-source AI models to segment bilateral gonads at MRI scans in healthy subjects. Materials and Methods: This study uses a longitudinal dataset consisting of 182 MRIs from 22 healthy girls (median age 13) and 266 MRIs from 44 healthy boys (median age 13) from a single institute. MRI acquisition included T2-weighted (T2W) sequence, along with fat-saturated (FS) T2W when indicated. An expert radiologist segmented gonadal anatomy, including ovarian cysts (>3 cm). Three-dimensional nnUnet models were trained for ovary, cyst, and testicle segmentation, respectively. The ovary–cyst segmentation model was applied to an external dataset with 30 adult subjects. Model performance was evaluated on the test set using the Dice similarity coefficient for ovary (DSCOV), cyst (DSCCY), and testicles (DSCTS). Subject-level total volumes for ovaries (TOV), cysts (TCV), and testicles (TTV) were computed. Results: Ovary, cyst, and testicle segmentation models achieved DSCOV of 0.86, DSCCY of 0.69, and DSCTS of 0.90 in the in-house test set, respectively. Average mean difference with 95% confidence intervals for TOV, TCV, and TTV were 0.87 (−5.78, 7.5), −0.41 (−3.3, 2.5), and 0.19 (−1.5, 1.9) cm³, respectively. Conclusions: The developed models show promising and reliable performance in volumetric and morphologic evaluation of gonads during puberty.

Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography - Diagnostic and Interventional Radiology

Prostate cancer (PCa) is the second most common cancer in men and has a significant health and social burden, necessitating advances in early detection, prognosis, and treatment strategies. Improvement in medical imaging has significantly impacted early PCa detection, characterization, and treatment planning. However, with an increasing number of patients with PCa and comparatively fewer PCa imaging experts, interpreting large numbers of imaging data is burdensome, time-consuming, and prone to variability among experts. With the revolutionary advances of artificial intelligence (AI) in medical imaging, image interpretation tasks are becoming easier and exhibit the potential to reduce the workload on physicians. Generative AI (GenAI) is a recently popular sub-domain of AI that creates new data instances, often to resemble patterns and characteristics of the real data. This new field of AI has shown significant potential for generating synthetic medical images with diverse and clinically relevant information. In this narrative review, we discuss the basic concepts of GenAI and cover the recent application of GenAI in the PCa imaging domain. This review will help the readers understand where the PCa research community stands in terms of various medical image applications like generating multi-modal synthetic images, image quality improvement, PCa detection, classification, and digital pathology image generation. We also address the current safety concerns, limitations, and challenges of GenAI for technical and clinical adaptation, as well as the limitations of current literature, potential solutions, and future directions with GenAI for the PCa community.

Upper limb amputation can severely restrict an individual’s ability to perform daily tasks. Myoelectric prostheses aim to restore the functionality of amputated limbs by utilizing signals from the remaining muscles in the residual limb. However, achieving high pattern recognition accuracy from acquired surface electromyography (sEMG) signals is a complex challenge. Several significant factors—including electrode position shifts during donning and doffing, inter-user variability, and issues related to muscle sweating—pose obstacles to attaining an accuracy rate exceeding 90%. These challenges can result in the production of prosthetic hands that do not function as intended. To address these issues, modified machine learning schemes and deep learning algorithms can be implemented to find optimal solutions. This review serves as a valuable resource for researchers, helping them understand the challenges associated with electrode position shifts and the solutions developed to enhance the performance of sEMG-based pattern recognition systems. It also reviews the causes of electrode position shifts, techniques for identifying shifts, and methods to mitigate the problems caused by electrode displacement in traditional sEMG-based pattern recognition. Additionally, the pros and cons of each technique are discussed based on window size, number of electrodes, features, gesture numbers, and types of algorithm performance of each technique. This work will also help researchers identify current research challenges related to electrode position shift problems and the solutions that have been proposed to overcome performance limitations.

The world of biomedical electronics is changing rapidly with the addition of newer circuit components and newer fabrication technologies day by day. The portability of biomedical devices is challenging due to size and battery life concerns and smaller geometries the poor noise performance and lack of linearity in performance are another big challenge. Operational transconductance amplifiers (OTAs) have recently gained much popularity in biomedical applications due to their requirement of low power and low voltage for operation yet offering low noise and good linearity at output. OTAs have also found wide application in data converters, sensors and signal processors including analog processing blocks as well. This study examines the behavior of previously reported CMOS OTAs in various literature works, based on their operational characteristics. Different OTA configurations are compared on the basis of their linearity, design technology, supply voltage, power consumption, cut-off frequency and bandwidth specifically for biomedical signal processing. This paper is a useful resource for researchers who wish to design OTA-based different circuits for biomedical applications and need a summary of performance characteristics of different types of OTAs reported so far.

Diabetic sensorimotor polyneuropathy (DSPN) is a serious long-term complication of diabetes, which may lead to foot ulceration and amputation. Among the screening tools for DSPN, the Michigan neuropathy screening instrument (MNSI) is frequently deployed, but it lacks a straightforward rating of severity. A DSPN severity grading system has been built and simulated for the MNSI, utilizing longitudinal data captured over 19 years from the Epidemiology of Diabetes Interventions and Complications (EDIC) trial. Machine learning algorithms were used to establish the MNSI factors and patient outcomes to characterise the features with the best ability to detect DSPN severity. A nomogram based on multivariable logistic regression was designed, developed and validated. The extra tree model was applied to identify the top seven ranked MNSI features that identified DSPN, namely vibration perception (R), 10-gm filament, previous diabetic neuropathy, vibration perception (L), presence of callus, deformities and fissure. The nomogram’s area under the curve (AUC) was 0.9421 and 0.946 for the internal and external datasets, respectively. The probability of DSPN was predicted from the nomogram and a DSPN severity grading system for MNSI was created using the probability score. An independent dataset was used to validate the model’s performance. The patients were divided into four different severity levels, i.e., absent, mild, moderate, and severe, with cut-off values of 10.50, 12.70 and 15.00 for a DSPN probability of less than 50, 75 and 100%, respectively. We provide an easy-to-use, straightforward and reproducible approach to determine prognosis in patients with DSPN.

Surface electromyography (EMG) is one of the promising signals for the recognition of the intended hand movement of an amputee. Nevertheless, there are several barriers to its successful implementation in the advanced prosthetic hand. Subject-dependent EMG pattern recognition is one of them, which limits the use of a training model for a specific subject to others. So, this study aims to explore a subject invariant EMG pattern recognition method that is performed by extracting subject invariant features. To extract subject invariant features, we have created a feature space using a feature extraction method, and the dimensionality of the feature space is reduced by employing spectral regression discriminant analysis (SRDA). Finally, each SRDA feature is normalized using min-max normalization, which confines the scale of each SRDA feature from 0 to 1. The proposed subject invariant EMG pattern recognition method achieves the F1 score of 97.26%, 96.47%, 95.42%, and 93.71% with a linear discriminant analysis classifier (LDA) for an electrode array of 8×16, 8×8, 8×4, and 8×2, respectively. The achieved performances are almost equal to or sometimes better than those achieved in subject independent and subject-dependent EMG pattern recognition. Also, the proposed method is simple, classifier independent, time complexity free, and does not require any customization or fine-tuning of classifiers. So, the proposed subject invariant EMG pattern recognition method would be an option to overcome the training barrier for each subject without compromising the EMG pattern recognition performance.

Backgound: The burden of pelvic organ prolapse in parous women is enormous. Bangladeshi women suffers a lot due to (POP) pelvic organ prolapse. Minor prolapse or laxity is generally ignored. When serious functional disability develops, women seek treatment advice. Home delivary and subinvolutions during puerperium is thought to predispose to urogenital prolapse. Here the incidence of different degree of prolapse and associated risk factors are assessed. Materials and method; This prospective observational study was carried out in outpatient department of gynaecology and obstetrics of SSMCMH during the period of January 2018 to September 2018. 100 parous women with subjective symptom of prolapse were included. Degree of prolapse and related risk factors were identified. Results: Here 53% had Pelvic Organ Prolapse Quantification System (POP-Q) grade 1,32% had grade II and 15% had grade III and IV. Majority showed anterior vaginal wall defect. Cystocoele was 77% and perineal tear was 79%. Rectocoele was also present in 60% cases. Risk increases with increasing age 45% was >60 years. Multipara (>4 child) had the highest incidence. Also H/O home delivery, delivery by untrained personal, puerperial unusual work load increases the risk. Conclusion : POP is a common condition. Here the prevalence of grade I and II were high. But grade III and IV requiring surgical treatment among the women was 12% and 3% respectively .Incidence rises with increasing age, parity, prolonged and difficult labour . Birth spacing <2 years and neglected puerperial period also contribute. Sir Salimullah Med Coll J 2022; 30: 129-132

Type 1 diabetes mellitus (T1DM) patients are a significant threat to chronic kidney disease (CKD) development during their life. However, there is always a high chance of delay in CKD detection because CKD can be asymptomatic, and T1DM patients bypass traditional CKD tests during their routine checkups. This study aims to develop and validate a prediction model and nomogram of CKD in T1DM patients using readily available routine checkup data for early CKD detection. This research utilized 1375 T1DM patients’ sixteen years of longitudinal data from multi-center Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials conducted at 28 sites in the USA and Canada and considered 17 routinely available features. Three feature ranking algorithms, extreme gradient boosting (XGB), random forest (RF), and extremely randomized trees classifier (ERT), were applied to create three feature ranking lists, and logistic regression analyses were performed to develop CKD prediction models using these ranked feature lists to identify the best performing top-ranked features combination. Finally, the most significant features were selected to develop a multivariate logistic regression-based CKD prediction model for T1DM patients. This model was evaluated using sensitivity, specificity, accuracy, precision, and F1 score on train and test data. A nomogram of the final model was further generated for easy application in clinical practices. Hypertension, duration of diabetes, drinking habit, triglycerides, ACE inhibitors, low-density lipoprotein (LDL) cholesterol, age, and smoking habit were the top-8 features ranked by the XGB model and identified as the most important features for predicting CKD in T1DM patients. These eight features were selected to develop the final prediction model using multivariate logistic regression, which showed 90.04% and 88.59% accuracy in internal and test data validation. The proposed model showed excellent performance and can be used for CKD identification in T1DM patients during routine checkups.