Abdominal Ultrasound Imaging Research Articles

Abstract 4131622: Opportunistic Screening of Chronic Liver Disease With Deep Learning Enhanced Echocardiography

Introduction: Chronic liver disease affects more than 1.5 billion adults worldwide, but the majority of cases are asymptomatic and undiagnosed. Echocardiography is broadly performed and visualizes the liver; however, this information is not diagnostically leveraged. Hypothesis and Aims: We hypothesized that a deep-learning algorithm can detect chronic liver diseases using subcostal echocardiography images that contains hepatic tissue. To develop and evaluate a deep learning algorithm on subcostal echocardiography videos to enable opportunistic screening for chronic liver disease. Methods: We identified adult patients who received echocardiography and abdominal imaging (either abdominal ultrasound or abdominal magnetic resonance imaging) with ≤30 days between tests. A convolutional neural network pipeline was developed to predict the presence of cirrhosis or steatotic liver disease (SLD) using echocardiogram images. The model performance was evaluated in a held-out test dataset, dataset in which diagnosis was made by magnetic resonance imaging, and external dataset. Results: A total of 2,083,932 echocardiography videos (51,608 studies) from Cedars-Sinai Medical Center (CSMC) were used to develop EchoNet-Liver, an automated pipeline that identifies high quality subcostal images from echocardiogram studies and detects presence of cirrhosis or SLD. In a total of 11,419 quality-controlled subcostal videos from 4,849 patients, a chronic liver disease detection model was able to detect the presence of cirrhosis with an AUC of 0.837 (0.789 - 0.880) and SLD with an AUC of 0.799 (0.758 - 0.837). In a separate test cohort with paired abdominal MRIs, cirrhosis was detected with an AUC of 0.726 (0.659-0.790) compared to MR elastography and SLD was detected with an AUC of 0.704 (0.689-0.718). In the external test cohort of 66 patients (n = 130 videos), the model detected cirrhosis with an AUC of 0.830 (0.738 - 0.909) and SLD with an AUC of 0.768 (0.652 – 0.875). Conclusions: Deep learning assessment of clinically indicated echocardiography enables opportunistic screening of SLD and cirrhosis. Application of this algorithm may identify patients who may benefit from further diagnostic testing and treatment for hepatic disease.

Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications.

In recent years, the integration of artificial intelligence (AI) techniques into medical imaging has shown great potential to transform the diagnostic process. This review aims to provide a comprehensive overview of current state-of-the-art applications for AI in abdominal and pelvic ultrasound imaging. We searched the PubMed, FDA, and ClinicalTrials.gov databases for applications of AI in abdominal and pelvic ultrasound imaging. A total of 128 titles were identified from the database search and were eligible for screening. After screening, 57 manuscripts were included in the final review. The main anatomical applications included multi-organ detection (n = 16, 28%), gynecology (n = 15, 26%), hepatobiliary system (n = 13, 23%), and musculoskeletal (n = 8, 14%). The main methodological applications included deep learning (n = 37, 65%), machine learning (n = 13, 23%), natural language processing (n = 5, 9%), and robots (n = 2, 4%). The majority of the studies were single-center (n = 43, 75%) and retrospective (n = 56, 98%). We identified 17 FDA approved AI ultrasound devices, with only a few being specifically used for abdominal/pelvic imaging (infertility monitoring and follicle development). The application of AI in abdominal/pelvic ultrasound shows promising early results for disease diagnosis, monitoring, and report refinement. However, the risk of bias remains high because very few of these applications have been prospectively validated (in multi-center studies) or have received FDA clearance.

Feature detection of B-ultrasound images of intussusception in children based on improved YOLOv8n

To assist grassroots sonographers in accurately and rapidly detecting intussusception lesions from children's abdominal ultrasound images, this paper proposes an improved YOLOv8n children's intussusception detection algorithm, called EMC-YOLOv8n. Firstly, the EfficientViT network with a cascaded group attention module was used as the backbone network to enhance the speed of target detection. Secondly, the improved C2fMBC module was used to replace the C2f module in the neck network to reduce network complexity, and the coordinate attention (CA) module was introduced after each C2fMBC module to enhance attention to positional information. Finally, experiments were conducted on the self-built dataset of intussusception in children. The results showed that the recall rate, average detection accuracy (mAP@0.5) and precision of the EMC-YOLOv8n algorithm improved by 3.9%, 2.1% and 0.9%, respectively, compared to the baseline algorithm. Despite slightly increased network parameters and computational load, significant improvements in detection accuracy enable efficient completion of detection tasks, demonstrating substantial economic and social value.

Nephrectomy in a rabbit with renal adenocarcinoma

The following case report describes the clinical approach concerning diagnostic procedures and treatment of a renal adenocarcinoma in a 2-year-old rabbit presented due to hyporexia and polydipsia. The clinical examination disclosed an abdominal mass. Further imaging revealed a unilateral enlargement of the left kidney presumed to be of neoplastic origin. After staging via blood testing, abdominal ultrasound and radiographic imaging of the thorax and abdomen revealed no signs of metastasis, nephrectomy was performed. Patho-histological examination confirmed the diagnosis of a renal adenocarcinoma. Despite its guarded prognosis the patient recovered completely and was free of metastases or other health limitations 12 months after the surgery.

Application of the mesh bridging technique in the excision of abdominal endometriosis lesions: Case report and literature review.

Abdominal wall scar endometriosis (AWE) is a rare endometriosis that usually occurs after gynecological or obstetric surgery and for which surgical resection is the standard treatment. For large tissue defects after resection, abdominal wall reconstruction is needed. Here, we describe a mesh bridging technique using biological and polypropylene meshes for abdominal wall reconstruction. A 34-year-old woman visited the center with complaints of low abdominal wall pain during menstruation for more than 5 years. Her surgical history included undergoing a cesarean section delivery twice. A mass measuring 6 cm × 5 cm × 3 cm was found above the symphysis pubis in the lower part of the abdominal incision. Endometriosis lesion was considered based on abdominal ultrasound and magnetic resonance imaging findings. After a multidisciplinary discussion that included surgical experts and gynecologists, the decision was made to perform abdominal endometrial focus excision plus abdominal wall reconstruction. Two kinds of mesh were skillfully used in the operation of this patient. Biological mesh was used close to the peritoneal side and covered with polypropylene mesh to reduce the stimulation by the polypropylene mesh of the peritoneum, enhance the strength of the biological mesh, and reduce the incidence of abdominal wall hernia. Our case demonstrates that accurate diagnosis of AWE followed by complete resection and reconstruction of the abdominal wall using a combination of biological and polypropylene mesh bridging can achieve good therapeutic results and patient satisfaction.

Primary Paraganglioma of the Prostate: A Systematic Review of the Literature for A Rare Entity.

Paragangliomas of the urinary tract are exceptionally uncommon, and sporadic case reports of primary paraganglioma of the prostate have been reported in the literature. Systematic research in PubMed/Medline and Scopus databases concerning primary prostatic paraganglioma was performed by two independent investigators. This analysis included 25 adult males, with a mean age of 49.8 ± 22.4 years. 32% of included patients had a history of hypertension. Problems during urination (52%), blood loss (44%), either as hematuria or hemospermia, and catecholamine-related symptoms (36%) comprised the most frequently reported clinical manifestations. Digital rectal examination found a palpable nodule in 36% of patients, while prostatic specific antigen (PSA) was normal in all tested patients. Abdominal ultrasound (44%), computed tomography (44%) and magnetic resonance imaging (28%) helped to identify the primary lesion. 24-hour urine epinephrine, norepinephrine and vanillylmandelic acid (VMA) levels were elevated in 90%, 80% and 90% of included patients. Open surgical excision of the mass was performed in 40%, transurethral resection in 8%, open radical prostatectomy in 24%, transurethral resection of the prostate in 16% and robot-assisted radical prostatectomy in 4% of included patients. Due to atypical clinical manifestation and scarcity of prostatic paraganglioma, urologists should be aware of this extremely rare entity.

Highly Elevated Alpha-fetoprotein Concentration in a Premature Infant

Alpha-1-fetoprotein (AFP) is a protein produced by the liver. Although its biological function is not fully understood, it is often used as a marker for cancer. High levels of AFP are also observed in premature infants. Reference ranges for AFP levels in premature infants are not specified. The authors present a case of a male newborn born at 25+5/7 weeks of gestational age. The boy underwent oncological diagnostics due to the detection, on the 24th day of life, of a liver lesion in an abdominal ultrasound examination, as well as significantly elevated AFP levels (>430,000 ng/mL). With each passing day, the AFP level gradually decreased, and the suspicious lesion was not visualized on abdominal ultrasound or magnetic resonance imaging on the 59th day of life. Based on the conducted examinations, prematurity and its treatment were considered the cause of high AFP levels. The aim of the study was to present the diagnostic difficulties associated with the lack of AFP norms for premature infants.

An approach to managing gallbladder polyps for the general practitioner.

Gallbladder polyps are increasingly being identified due to the widespread use of abdominal ultrasound imaging. They are concerning lesions due to their potential malignant risk. It is hoped that managing them correctly will play a role in improving poor survival rates of gallbladder cancer. Awareness of these lesions is lacking. Management continues to be guided by expert opinion and observational studies and a number of consensus statements exist. This paper reviews and summarises the current literature and provides an approach for general practitioners based on the available guidance. Although minor variation exists between consensus statements, the risk of malignancy for gallbladder polyps is still largely dictated by size, with those ≤5 mm generally considered to pose little risk and not requiring follow-up, whereas those ≥10 mm considered at greater risk and requiring referral for cholecystectomy.

Opportunistic Screening of Chronic Liver Disease with Deep Learning Enhanced Echocardiography.

Chronic liver disease affects more than 1.5 billion adults worldwide, however the majority of cases are asymptomatic and undiagnosed. Echocardiography is broadly performed and visualizes the liver; but this information is not leveraged. To develop and evaluate a deep learning algorithm on echocardiography videos to enable opportunistic screening for chronic liver disease. Retrospective observational cohorts. Two large urban academic medical centers. Adult patients who received echocardiography and abdominal imaging (either abdominal ultrasound or abdominal magnetic resonance imaging) with ≤30 days between tests, between July 4, 2012, to June 4, 2022. Deep learning model predictions from a deep-learning computer vision pipeline that identifies subcostal view echocardiogram videos and detects the presence of cirrhosis or steatotic liver disease (SLD). Clinical diagnosis by paired abdominal ultrasound or magnetic resonance imaging (MRI). A total of 1,596,640 echocardiogram videos (66,922 studies from 24,276 patients) from Cedars-Sinai Medical Center (CSMC) were used to develop EchoNet-Liver, an automated pipeline that identifies high quality subcostal images from echocardiogram studies and detects the presence of cirrhosis or SLD. In the held-out CSMC test cohort, EchoNet-Liver was able to detect the presence of cirrhosis with an AUC of 0.837 (0.789 - 0.880) and SLD with an AUC of 0.799 (0.758 - 0.837). In a separate test cohort with paired abdominal MRIs, cirrhosis was detected with an AUC of 0.704 (0.689-0.718) and SLD was detected with an AUC of 0.726 (0.659-0.790). In an external test cohort of 106 patients (n = 5,280 videos), the model detected cirrhosis with an AUC of 0.830 (0.738 - 0.909) and SLD with an AUC of 0.768 (0.652 - 0.875). Deep learning assessment of clinical echocardiography enables opportunistic screening of SLD and cirrhosis. Application of this algorithm may identify patients who may benefit from further diagnostic testing and treatment for chronic liver disease.

Validation of a deep-learning modular prototype to guide novices to acquire diagnostic ultrasound images from urinary system

ImportanceArtificial intelligence (AI) application in guiding the acquisition of ultrasonography images represents a pioneering field of research. A new developed hybrid deep-learning (DL) algorithm, trained on more than high quality 60.000 curated and labelled reference images (distilled from a set of more than 600.000 abdominal ultrasound images) from La Paz Hospital, can provide real-time prescriptive guidance for novice operators to obtain standard planes images of the target organs. ObjectiveThis study aims to evaluate the capability of novice users to acquire diagnostic-quality abdominal ultrasound images of the urinary system using the deep-learning (DL)-based guiding research prototype provided by GMV. DesignSetting, and Participants: This prospective diagnostic study was conducted within the facilities of an academic hospital. A cohort of 24 technically-oriented volunteers, lacking prior knowledge in anatomy or medicine and without experience in conducting ultrasound examinations, was recruited. After a brief training session focused on various organs, each pair of volunteers performed scans of each other's urinary system, exclusively guided by AI support. These scans were subsequently repeated by experienced sonographers using identical ultrasound equipment but without AI assistance. Four radiologists, each with decades of experience, independently and blindly assessed the quality of each acquisition. ResultsOver 90.6 % of the images scanned by volunteers were identified as valuable clinical picture, using only an AI-based guidance system. This is nearly comparable to the results achieved by experienced radiologists, who attained a 98.6 % success rate. ConclusionsThis deep-learning (DL) prototype enables novices lacking experience in ultrasonography to acquire diagnostic ultrasound images suitable for subsequent expert evaluation. The modular prototype works on a large range of ultrasound device models and vendors. This advancement has the potential to extend the application of ultrasound beyond traditional clinical environments, particularly in situations requiring immediate anatomical and functional interrogation, as well as in resource-limited settings.

Hydrocolpos causing bowel obstruction in a preterm newborn: a case report

BackgroundImperforate hymen is the most common congenital defect of the female urogenital tract. The spectrum of clinical manifestations is broad, ranging from mild cases undiagnosed until adolescence to severe cases of giant intraabdominal masses. The most common complication of hydrocolpos is bladder compression, resulting in obstructive uropathy and hydronephrosis.Case presentationWe present here the case of a preterm neonate who was admitted to the surgical neonatal intensive care unit for bowel obstruction. The baby did not appear septic or unwell, a small amount of meconium passed frequently, and no bilious gastric residuals occurred. Based on these findings, acute abdominal obstruction was doubtful, and the surgeon chose a conservative (watch and wait) approach. Subsequently, we performed abdominal ultrasound and magnetic resonance imaging based on unclear information about a suspicious abdominal mass raised by the gynecologist shortly before the emergency C-section. The final diagnosis was congenital hydrocolpos due to imperforate hymen. The pediatric gynecologist indicated an incision of the imperforate hymen under general anesthesia. The incision resolved abdominal distention as well as the bowel obstruction.ConclusionThe presentation of hydrocolpos was not typical (no bulging in the vaginal introitus) in our case, and clinical symptoms implied acute bowel obstruction shortly after birth. The surgeon chose a conservative (watch and wait) approach as the baby did not appear unwell on the second day of life. Fortunately, diagnostic laparotomy was not required as the next step in bowel obstruction management. All clinical symptoms resolved after a minor surgical procedure.

Assessment of left atrial function by two-dimensional speckle tracking echocardiography in patients with metabolic-associated fatty liver disease

BackgroundMetabolic-associated fatty liver disease (MAFLD) and cardiovascular diseases have mutual risk factors that contribute to pathogenic processes, increasing mortality and morbidity. This study aimed to evaluate variations in left ventricular (LV) structure and diastolic function among different subtypes and severity degrees of MAFLD patients, allowing early identification, intervention, and prevention of severe cardiac outcomes in high-risk populations.ResultsThe cross-sectional study included 142 MAFLD patients and 142 non-MAFLD participants as a control group. All participants underwent abdominal ultrasound, transient elastography, transthoracic echocardiography, tissue Doppler, and strain imaging. The results showed a significant impairment in the diastolic left ventricular function, as assessed with tissue Doppler, and the left atrial (LA) function, as evaluated with strain imaging, in the MAFLD group. Additionally, the left atrial stiffness was significantly higher in the MAFLD group.ConclusionThe use of strain imaging facilitated the detection of subtle impairments of the left atrial reservoir, contraction, conduit function, and left ventricular diastolic function in MAFLD patients.

In vivo bistatic dual-aperture ultrasound imaging and elastography of the abdominal aorta.

Introduction: In this paper we introduce in vivo multi-aperture ultrasound imaging and elastography of the abdominal aorta. Monitoring of the geometry and growth of abdominal aortic aneurysms (AAA) is paramount for risk stratification and intervention planning. However, such an assessment is limited by the lateral lumen-wall contrast and resolution of conventional ultrasound. Here, an in vivo dual-aperture bistatic imaging approach is shown to improve abdominal ultrasound and strain imaging quality significantly. By scanning the aorta from different directions, a larger part of the vessel circumference can be visualized. Methods: In this first-in-man volunteer study, the performance of multi-aperture ultrasound imaging and elastography of the abdominal aortic wall was assessed in 20 healthy volunteers. Dual-probe acquisition was performed in which two curved array transducers were aligned in the same imaging plane. The transducers alternately transmit and both probes receive simultaneously on each transmit event, which allows for the reconstruction of four ultrasound signals. Automatic probe localization was achieved by optimizing the coherence of the trans-probe data, using a gradient descent algorithm. Speckle-tracking was performed on the four individual bistatic signals, after which the respective axial displacements were compounded and strains were calculated. Results: Using bistatic multi-aperture ultrasound imaging, the image quality of the ultrasound images, i.e., the angular coverage of the wall, was improved which enables accurate estimation of local motion dynamics and strain in the abdominal aortic wall. The motion tracking error was reduced from 1.3 mm ± 0.63 mm to 0.16 mm ± 0.076 mm, which increased the circumferential elastographic signal-to-noise ratio (SNRe) by 12.3 dB ± 8.3 dB on average, revealing more accurate and homogeneous strain estimates compared to single-perspective ultrasound. Conclusion: Multi-aperture ultrasound imaging and elastography is feasible in vivo and can provide the clinician with vital information about the anatomical and mechanical state of AAAs in the future.

Abdominal multi-organ segmentation using multi-scale and context-aware neural networks

Recent advancements in AI have significantly enhanced smart diagnostic methods, bringing us closer to achieving end-to-end diagnosis. Ultrasound image segmentation plays a crucial role in this diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we consider two inherent features of ultrasound images: (1) different organs and tissues vary in spatial sizes, and (2) the anatomical structures inside the human body form a relatively constant spatial relationship. Based on those two ideas, we proposed two segmentation models combining multi-scale convolution neural network backbones and a spatial context feature extractor. We discuss two backbone structures to extract anatomical structures of different scales: the Feature Pyramid Network (FPN) backbone and the Trident Network backbone. Moreover, we show how Spatial Recurrent Neural Network (SRNN) is implemented to extract the spatial context features in abdominal ultrasound images. Our proposed model has achieved dice coefficient score of 0.919 and 0.931, respectively.

Innovative approach: utilizing silver nanoparticles sheet for improved rabbit cecal anastomosis healing.

Anastomotic leakage is a severe complication associated with gastrointestinal surgery. The process of intestinal wound healing is crucial for the successful outcome of digestive tract surgical repair procedures. This research aimed to determine the impact of silver nanoparticles sheet (Acticoat) on the anastomotic healing of the cecum in rabbits. A total of 48 New Zealand male rabbits in good health were used for cecum transection and anastomosis. The animals were randomized into the control group (C) and the silver nanoparticles group (AgNPs). In the C group, the transected cecum was end-to-end anastomosed with a single layer of simple continuous suture pattern using 3-0 polyglyconate. In contrast, a silver nanoparticle sheet (Acticoat) was covered around the sutured anastomotic line in the AgNPs group. Postoperatively, abdominal ultrasound imaging and the Bristol Rabbit Pain Score (BRPS) were measured on days 7, 15, and 30. Eight rabbits from each group were euthanized at each time point to assess macroscopic findings, bursting pressure tests, tensile strength tests, histopathological examinations, and immunohistochemical analyses. The AgNPs group demonstrated a significant increase in the cecal lumen diameter wall (p ≤ 0.001), burst pressure measurement (p ≤ 0.02), and tensile strength (p ≤ 0.01). Conversely, the AgNPs group had significantly lower BRPS scores (p ≤ 0.01). In addition, histopathological examinations revealed that AgNPs significantly reduced inflammatory cell infiltration (neutrophils and macrophages) and enhanced collagen deposition. Immunohistochemical analyses revealed a significant increase (p ≤ 0.01) of α-SMA and a reduction of CD31 in the anastomotic tissue of the AgNPs group. The results of the present study indicate that the utilization of the AgNPs sheet (Acticoat®) effectively enhanced the strength of cecum anastomosis, resulting in a reduction in anastomosis leakages, pain scores, and abdominal adhesions. Additionally, the bursting pressure values in the rabbit model were significantly increased.

[Applications] 9. Development of an AI System for Liver Tumor Detection from Abdominal Ultrasound Images

[Applications] 9. Development of an AI System for Liver Tumor Detection from Abdominal Ultrasound Images

Investigation of the Prevalence of Cholelithiasis in Liver Cirrhosis Cases and Controls on Upper Abdominal Ultrasound Images

Investigation of the Prevalence of Cholelithiasis in Liver Cirrhosis Cases and Controls on Upper Abdominal Ultrasound Images

A rare case of localized peliosis hepatis during adjuvant chemotherapy including oxaliplatin mimicking a liver metastasis of colon cancer

BackgroundOxaliplatin-based regimens are commonly used as adjuvant chemotherapy following surgery for colorectal cancer. Adverse events associated with oxaliplatin include blue liver, which is caused by sinusoidal dilation and diffuse peliosis hepatis. We report herein a case of localized peliosis hepatis closely resembling a metastatic liver tumor.Case presentationThe patient, a 50-year-old male, underwent a robotically assisted colectomy for rectosigmoid colon cancer, which was discovered when hematochezia occurred. The patient received a diagnosis of pStage IIIb and was treated with four courses of CAPOX as adjuvant chemotherapy starting at postoperative month 1. At postoperative month 4, contrast-enhanced computed tomography (CT) of the abdomen revealed a 20-mm, low-density area with heterogeneous internal structure in S6/7 of the liver. Abdominal ultrasound and gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid-enhanced magnetic resonance imaging (EOB-MRI) findings led to a diagnosis of metastatic liver tumor, for which a laparoscopic partial hepatectomy was performed. The resected lesion was a dark reddish-brown nodule with indistinct margins that appeared to be continuous with the surrounding area. Histopathological analysis revealed severe, localized dilatation of the sinusoids and congestion consistent with the gross nodule. Based on these findings, localized peliosis hepatis associated with oxaliplatin-induced sinusoidal damage was diagnosed.ConclusionsLocalized peliosis hepatis associated with oxaliplatin use can be difficult to distinguish from a metastatic liver tumor on imaging studies.

Relevant edge probability‐based adaptively weighted active contour for medical image segmentation

AbstractThe level‐set based active contours have been found popular for medical image segmentation tasks, because of their inherent support for the topological changes—splitting and merging. Meanwhile, contour's leakage through the weak edges and premature convergence due to intensity inhomogeneity diminish its accuracy. Adjusting energy weights according to image features, local to the contour can be helpful. However, weight adjusted as deterministic function of the features is not adequate, limiting the segmentation accuracy. To address the problem, a new relevant edge probability based adaptively weighted level‐set evolution (REP‐WLSE) method for medical image segmentation is investigated. The weights used in this proposal are adaptive to an image relative value, obtained statistically from the feature‐explorations during the contour's evolution. The value is basically an estimate of contour's probability of finding relevant boundary edges on the image plane. Spatial intensity‐range filtering provides the feature space. An adaptive time‐step management scheme is also implemented, which controls the speed variation of the contour evolution. Time‐step is adjusted as a function of contour's alignment with the edges. The merits of the suggested methodology are—(i) reduced leakage through the weak edges, (ii) ability to handle the inhomogeneity, and (iii) increased chances of convergence around the foreground/region of interest (ROI). Experimental results using brain MR, abdominal ultrasound, and breast ultrasound images are presented. State‐of‐the‐art methods are compared using different metrics. The suggested methodology achieved better results.

Semantic segmentation and classification of polycystic ovarian disease using attention UNet, Pyspark, and ensemble learning model

AbstractOvarian abnormality like polycystic ovarian disease (PCOD) is one of the most common diseases among women worldwide. PCOD not only has an impact on infertility but also hurts the psychological well‐being of women affecting their quality of life. In this study, a two‐class pattern learning problem is designed for the classification of PCOD. In total, 37 clinical parameters and abdominal ultrasound images of women are collected under the proper ethical protocol. Using only clinical data, an accuracy of 93.7% is obtained using Random Forest as the classifier which is further improved to 95.54% by using a Randomized Search CV during Random Forest classification. The ultrasound images are classified using the proposed Attention‐UNet architecture and a mean Dice score of 0.945 is obtained indicating more accurate segmentation. The segmented images are passed through the state‐of‐the‐art EfficientNet B6 for the classification of PCOS and non‐PCOS and recorded an accuracy of 95.47%. Using big data architecture Pyspark, the performance is further enhanced to 96.8% and 96.3% for clinical and ultrasound images respectively along with the reduced computational speed. The results of these classifiers are then used to create metadata and a customized Artificial Neural Network is applied for the final prediction of PCOD and non‐PCOD. From the results, it can be seen that the stacking model outperformed with an accuracy of 98.12% when compared to the single classifier. Our proposed method has very good performance with less computation, contributing a new architecture to evaluate PCOD and hence helping to improve the wellness of women.