Identification Of Fractures Research Articles

A Simulation Model for the Handheld Ultrasound Diagnosis of Pediatric Forearm Fractures.

Handheld ultrasound (HHU) devices have gained prominence in emergency care settings and post-graduate training, but their application in the diagnosis of pediatric fractures remains under-explored. The aim of this study is to evaluate the effectiveness and accuracy of an HHU device for diagnosing pediatric forearm fractures using a simulation model. The materials for the basic pediatric fracture model include turkey bones soaked in white vinegar to make them pliable, food-grade gelatine, and plastic containers. Ultrasound analysis of the models was done with an HHU device, Sonosite İViz US (FUJIFILM Sonosite, Inc.; Bothell, Washington USA). Four different fracture patterns (transverse fracture, oblique fracture, greenstick fracture, and a torus fracture) and one model without fracture were used in this study. Twenty-six Emergency Medicine residents sonographically evaluated different bone models in order to define the presence and absence of fracture and the fracture subtype. The participants' ability to obtain adequate images and the time taken to create and recognize the images were evaluated and recorded. After the sonographic examination, the residents were also asked for their opinion on the model as a teaching tool. All participants (100%) recognized the normal bone model and the fracture, regardless of the fracture type. The consistency analysis between the practitioners indicated a substantial agreement (weighted kappa value of 0.707). The duration to identify the target pathology in fracture models was significantly longer for the greenstick fracture (78.57 [SD = 30.45] seconds) model compared to other models. The majority of participants (92.3%) agreed that the model used would be a useful teaching tool for learning ultrasound diagnosis of pediatric forearm fractures. All participants successfully identified both the normal bone model and the presence of fractures, irrespective of the fracture type. Significantly, the identification of the greenstick fracture took longer compared to other fracture types. Moreover, the majority of participants acknowledged the model's utility as a teaching tool for learning ultrasound diagnosis of pediatric forearm fractures.

241 ‘Please consider bone assessment’: improving the identification and management of Vertebral Fractures by a ‘Fracture Liaison Service’

Abstract Background Osteoporotic fractures are a major public health problem due to an ageing population causing profound disability and reduced quality of life. Vertebral Fractures (VFs) are the second most common osteoporotic fracture with nearly 1 in 4 women having one in their lifetime. Furthermore, a VF is a significant warning sign for another fracture in the following year—known as ‘imminent fracture risk’. Despite this early opportunity for clinical intervention with bone protective medication, VFs are frequently under-diagnosed and under-treated. This observational study aimed to look at the point prevalence of VFs picked up on radiology reports and compare with those identified by the ‘Fracture Liaison Service’ (FLS). Methods The PACs office ran a search for all MRI spine, CT-TAP and a plain radiographic film reports from January 2021 until April 2023 with the terms ‘vertebrae’ and ‘compression’ in the text. Inclusion criteria was all patients >65 without major trauma with a radiologist defined osteoporotic VF. Using excel, we cross-referenced the proportion of these already know to the FLS. Results There were 338 osteoporotic VFs diagnosed on radiological imaging between 2021 & 2023. Of these, 101 had been identified by the FLS. The median age was 77 years old. There were 228 (67.2%) females and 111 (32.8%) males. Conclusion This study helped to highlight that over 67.2% of the osteoporotic VFs are not being identified by the FLS. Therefore, is it assumed that timely intervention and management of their imminent fracture risk may not have been undertaken. This data is being used to drive a quality improvement project with the radiology department where any VF in women >70 will be reported to include—‘please consider bone assessment’.

Intelligent recognition and identification of fracture types and parameters for borehole images based on developed convolutional neural networks and post-processing

Borehole imaging is one of the main methods for fracture observation in the rock formations, but there is still a lack of identification systems for deriving fracture parameters from borehole images. Additionally, previous research were limited to sinusoidal fractures. In this study, an intelligent system is developed for multiple types of qualitative fracture identification in a quick and robust manner using convolution neural networks and post-processing. Initially, borehole imaging was carried out in six coal mines to develop a borehole image database that includes a wide variety of rock types and fractures. Afterward, the acquired borehole images of fractures were categorized into three types, such as sinusoidal fractures, vertical fractures and broken areas. In this connection, dual convolutional neural networks were developed to recognize fracture type pixels. Meanwhile, the calculation models associated with corresponding fracture parameters were established to quantitatively describe the fracture characteristics. Eventually, the post-processing programs were developed to identify the geometric parameters for each fracture type. The results indicate that the developed dual convolutional neural networks has the capability to detect fracture pixels with a recognition accuracy of 77.1% for each sinusoidal and vertical fracture pixels, and recognication accuracy of 72.9% for broken area pixels. The post-processing program was developed based on Hough transform and a thinning algorithm, that enables it to the the average recognition rate of 88.71% with an average false alarm rate of 16.67% for all three types of fractures.

External validation of a convolutional neural network algorithm for opportunistically detecting vertebral fractures in routine CT scans.

To evaluate the performance of a previously trained Convolutional Neural Network (CNN) model to automatically detect vertebral fractures (VFs) in CT scans in an external validation cohort. Two Chinese studies and clinical data were used to retrospectively select CT scans of the chest, abdomen and thoracolumbar spine in men and women aged ≥50 years. The CT scans were assessed using the semiquantitative (SQ) Genant classification for prevalent VFs in a process blinded to clinical information. The performance of the CNN model was evaluated against reference standard readings by the area under the receiver operating characteristics curve (AUROC), accuracy, Cohen's kappa, sensitivity, and specificity. A total of 4,810 subjects were included, with a median age of 62 years (IQR 56-67), of which 2,654 (55.2%) were females. The scans were acquired between January 2013 and January 2019 on 16 different CT scanners from three different manufacturers. 2,773 (57.7%) were abdominal CTs. A total of 628 scans (13.1%) had ≥1 VF (grade 2-3), representing 899 fractured vertebrae out of a total of 48,584 (1.9%) visualized vertebral bodies. The CNN's performance in identifying scans with ≥1 moderate or severe fractures achieved an AUROC of 0.94 (95% CI: 0.93-0.95), accuracy of 93% (95% CI: 93%-94%), kappa of 0.75 (95% CI: 0.72-0.77), a sensitivity of 94% (95% CI: 92-96%) and a specificity of 93% (95% CI: 93-94%). The algorithm demonstrated excellent performance in the identification of vertebral fractures in a cohort of chest and abdominal CT scans of Chinese patients ≥50 years.

Two-Stage Structure-Focused Contrastive Learning for Automatic Identification and Localization of Complex Pelvic Fractures.

Pelvic fracture is a severe trauma with a high rate of morbidity and mortality. Accurate and automatic diagnosis and surgical planning of pelvic fracture require effective identification and localization of the fracture zones. This is a challenging task due to the complexity of pelvic fractures, which often exhibit multiple fragments and sites, large fragment size differences, and irregular morphology. We have developed a novel two-stage method for the automatic identification and localization of complex pelvic fractures. Our method is unique in that it allows to combine the symmetry properties of the pelvic anatomy and capture the symmetric feature differences caused by the fracture on both the left and right sides, thereby overcoming the limitations of existing methods which consider only image or geometric features. It implements supervised contrastive learning with a novel Siamese deep neural network, which consists of two weight-shared branches with a structural attention mechanism, to minimize the confusion of local complex structures of the pelvic bones with the fracture zones. A structure-focused attention (SFA) module is designed to capture the spatial structural features and enhances the recognition ability of fracture zones. Comprehensive experiments on 103 clinical CT scans from the publicly available dataset CTPelvic1K show that our method achieves a mean accuracy and sensitivity of 0.92 and 0.93, which are superior to those reported with three SOTA contrastive learning methods and five advanced classification networks, demonstrating the effectiveness of identifying and localizing various types of complex pelvic fractures from clinical CT images.

Effectiveness of fracture liaison services in osteoporosis.

In response to the gradual decline in the number of prescriptions for anti-osteoporosis medication (AOM) following fragility fractures, fracture liaison services (FLSs) have been set up around the world with the aim of filling this treatment gap. Several studies have already reported the benefits of such organizations, particularly in reducing fracture risk, mortality rates and healthcare costs, and literature on FLSs has increased at a steady pace over time. A narrative review was conducted on the latest available findings on the effectiveness of FLSs. Various approaches to implementing an effective FLS program are discussed. FLS programs have enhanced the management of osteoporosis-related fractures. However, several studies have highlighted that not all FLSs are necessarily effective in reducing subsequent fracture risk and mortality. Long-term AOM persistence and monitoring are another critical issue in FLS programs. A few studies have reported that FLSs are associated with an improvement in AOM persistence, regardless of the type of AOM. Practitioners in the FLS setting need to be aware of the impact of recency of fracture and fracture recurrence rates, and the need for timely interventions. The administration of zoledronic acid in an in-patient setting may improve AOM treatment rates in patients, who often encounter obstacles to outpatient follow-up. Introducing 'vertebral fracture identification services' in FLS programs is also an option. However, doing so leads to an increase in workload and this would need to be considered by any FLS that is considering introducing such a service. Evidence suggests that digital technologies can support (i) multidisciplinary teams in providing the best possible patient care based on current evidence, and (ii) patient self-management. However, as the methodological quality of many of the studies evaluating these technologies was poor, their validity of their results is limited. Further research should focus on the optimal implementation of post-fracture care using automated systems, and standardized reporting of patient's characteristics and outcome measures using key performance indicators.

Combining Register and Radiological Visits Data Allows to Reliably Identify Incident Wrist Fractures.

To evaluate how comprehensively wrist fractures can be tracked from the national medical registers, and to propose a method for complementing the register data using time stamps of wrist radiography visits recorded in the radiological image archive. For the Kuopio Osteoporosis Risk Factor and Prevention Study (OSTPRE) cohort of 14220 post-menopausal women, we analysed the data from the Care Register for Health Care, Register for Primary Health Care Visits, self-reports, radiological image archive PACS, and patient records to identify the wrist fractures occurred between 2011 and 2021. Using this gold standard of fractures, we validated the coverage of the registers and image archive and created algorithms to automatically identify fracture events from the registers and/or metadata of wrist radiography visits. We show that wrist fractures cannot be comprehensively identified based on national registers. To remedy this, our proposed method of combining register and image archive data can lift the coverage from 81% to 94% and reduce false discoveries from 6% to 2%. The proposed method offers a more reliable way of gathering fracture information. Comprehensive fracture identification is essential in many research settings, such as incidence statistics, prevention studies, and risk assessment models.

Frequency of mandibular fractures in children presenting with maxillofacial trauma.

Objective: To determine the frequency of mandibular fractures in children presenting with maxillofacial trauma. Study Design: Cross Sectional. Setting: Department of Oral and Maxillofacial, Ayub Dental Section at Ayub Teaching Hospital, Abbottabad. Period: 05 December 2018 to 05 June 2019. Material & Methods: A total number of 166 children from both genders between the ages of 1-12 years with a history of trauma to mandible were added to the study through consecutive non-probability sampling. All the particulars of these patients like age, gender, date of trauma, cause of trauma, pattern of trauma, associated injuries were noted down on a format designed for the study and X-rays of all the patients were taken. Results: The Mean±SDs of age in this study was 5.34+1.867. Out of these patients 96 (57.8%) were male while 70 (42.2%) were females. The results show that 66 (39.8%) patients had symphysis, 48 (29%) patients had parasymphysis, 32 (19.2%) patients had angle/body, 20 (12%) patients had condyle fractures. Associated injuries were also present in 103 (62%) patients. Conclusion: The most common type of mandibular fracture in children was symphysis followed by parasymphysis, angle/body and condyle fractures. Early identification and treatment of such pediatric mandibular fractures will help to minimize hospital stay thus reducing financial burden on patients.

560 Evaluating the Utility of Plain Radiograph and Computerised Tomography Scanning in Identifying Concomitant Foot Fractures in Patients with Unstable Lisfranc Injuries

Abstract Aim Lisfranc fracture dislocations are uncommon injuries. There is varying evidence on the utility of plain radiographs (XR) and CT in identifying Lisfranc injuries and concomitant fractures. Our aim was to identify the utility of XR as compared to CT, with the nul hypothesis that there was no difference in fracture identification. Method A retrospective assessment of patients who had sustained a Lisfranc injury between 2013 and 2022 across two trauma centres within the United Kingdom who underwent surgery. Pre-operative images were reviewed independently by 2 reviewers to identify the presence of injuries. Results A total of 175 patients were included. We found that XR images significantly under-diagnosed metatarsal and midfoot fractures. The largest discrepancies between XR and CT in their rates of detection were in fractures of the cuboid (5.7% vs 28%, p<0.001), medial cuneiform (20% vs 51%, p = 0.008), lateral cuneiform (4% vs 36%, p = 0.113), second metatarsal (57% vs 82%, p<0.001), third metatarsal (37% vs 61%, p<0.001) and fourth metatarsal (26% vs 43%, p<0.001). As compared to CT, the sensitivity of XR was low. The lowest sensitivity for identification however was lateral foot injuries, specifically fractures of the lateral cuneiform (sensitivity 7.94%, specificity 97.3%), cuboid (sensitivity 18.37%, specificity 99.21%), fourth (sensitivity 46.7%, specificity 89.80%) and fifth metatarsal (sensitivity 45.00%, specificity 96.10%). Conclusions From our analysis, we can determine that XR significantly under-diagnoses associated injuries in patient sustaining an unstable Lisfranc injury, with lateral foot injuries being the worst identified. We advised the use of CT imaging in all cases for appropriate surgical planning.

Red flags to screen for vertebral fracture in people presenting with low back pain.

Red flags to screen for vertebral fracture in people presenting with low back pain.

Research on dual lateral log simulation of shale bedding fractures under different influencing conditions

Micro scale fractures play a crucial role in facilitating the migration of oil and gas in low permeability shale reservoirs. However, the identification of such fractures is a complex task. The efficacy of the dual lateral log physical field changes as a means of identifying shale micro fractures remains uncertain. To address this issue, a three-layer shale bedding fracture model was developed using the finite element method and core and conventional logging data from eight wells, which was based on different shale reservoirs within the Yanchang Formation in the Ordos Basin. The present study examines the dual lateral log response characteristics resulting from distinct characteristics of horizontal bedding fractures, with a focus on the response mechanism. The simulation of logging response characteristics of shale and siltstone combination were utilized by core statistical data for verification purposes. The results indicate that under the lithology combination of shale and siltstone, the magnitude of the difference between the resistivity of the filled fracture and the formation resistivity, the greater the formation resistivity of the shale itself, the wider the fracture width, and the greater the difference between the apparent resistivity and the real formation resistivity. Furthermore, the suitable conditions for the detection of shale bedding fracture characteristics by dual lateral log are clarified. In the presence of effective filling, the dual lateral log has the capability to identify shale fractures at the micron scale. The findings of our study establish a theoretical framework for the identification and assessment of shale fractures, and furnish technical assistance for the optimal selection of “sweet spots” within shale reservoirs and the precise evaluation of reservoirs. This study lays a theoretical foundation for the identification and evaluation of shale fractures, and provides technical support for the optimal “sweet spot” selection of shale reservoirs and the accurate evaluation of reservoirs.

Non-pharmacological management of osteoporotic vertebral fractures: health-care professional perspectives and experiences

Purpose To understand experiences and perceptions on non-pharmacological treatment of vertebral fractures and virtual-care from the perspective of care professionals’ (HCPs) Design and setting We conducted semi-structured interviews with 13 HCPs within Canada (7 F, 6 M, aged 46 ± 12 years) and performed a thematic and content analysis from a post-positivism perspective. Results Two themes were identified: acuity matters when selecting appropriate interventions; and roadblocks to receiving non-pharmacological interventions. We found that treatment options were dependent on the acuity/stability of fracture and were individualized accordingly. Pain medication was perceived as important, but non-pharmacological strategies were also considered helpful in supporting recovery. Participants discussed barriers related to the timely identification of fracture, referral to physiotherapy, and lack of knowledge among HCPs on how to manage osteoporosis and vertebral fractures. HCPs reported positive use of virtual-care, but had concerns related to patient access, cost, and comprehensive assessments. Conclusion HCPs used and perceived non-pharmacological interventions as helpful and selected specific treatments based on the recency of fracture and patient symptoms. HCPs’ also believed that virtual-care that included an educational component, an assessment by a physiotherapist, and an exercise group was a feasible alternative, but concerns exist and may require further evaluation.

Identification of fractures on pediatric foot radiographs: do localization cues improve diagnostic accuracy and reduce interpretation time?

To investigate the diagnostic accuracy and time in the detection of fractures on pediatric foot radiographs marked without and with localization cues. One-hundred randomly selected foot radiographic examinations that were performed on children (<18 yearsold) after injury and withat least 4 weeks of follow-up were included. Blinded to history and diagnosis, 4 readers (one each: medical student, pediatrician,pediatric orthopedic surgeon, and pediatricmusculoskeletal radiologist) retrospectively and independently reviewed each examination twice (without and with cue, at least 1 month apart, and after randomization). Each reader recorded the presence or absence of a fracture, fracture location, diagnostic confidence, and the total (interpretation) time spent on each study. Diagnostic accuracy, reader confidence, and interpretation time were compared between examinations without and with cues. Our study included 59 examinations without and 41 with fractures (21 phalangeal, 18 metatarsal, and 2 tarsal fractures). Localization cues improved inter-reader agreement (κ=0.36 to 0.64), overall sensitivity (68 to 72%), specificity (66 to 73%), and diagnostic accuracy (67 to 73%); thus, overcalled and missed rates also improved from 34 to 27% and 32 to 28%, respectively. Reader confidence improved with cue (49 to 61%, p<0.01) with higher incremental improvement with younger children (30% for 1-6 years; 14% for 7-11 years; and 10% for 12-17 years). Interpretation time decreased by 40% per examination (40±22 s without to 24±13 s with cues, p<0.001). Localization cues improved diagnostic accuracyand reader confidence, reducing interpretation time in the detection of pediatric foot fractures.

Fracture identification in reservoirs using well log data by window sliding recurrent neural network

Detecting fractures using well logs can be difficult due to the complex response of conventional logs. To address this issue, a novel method called Fracture Identification by window sliding and recurrent neural network (FIsr) is proposed. FIsr uses window sliding to generate sequence image data for training a bidirectional recurrent neural network (BiLSTM) classifier, with columns selected from both conventional and reconstructed logs. Undersampling is applied to balance the data, as the number of fracture samples is much smaller than nonfracture samples. BiLSTM extracts features from the sequence data in two directions, considering label correlations and detecting local log anomalies caused by fractures. The prediction for each sample is based on multiple overlapping sequence images to reduce uncertainties. The proposed method is validated using a dataset from carbonate reservoirs of the Asmari Formation in the Middle East, with an accuracy of 95% and recall and precision metrics exceeding 90%. A blind well test shows that FIsr can detect all fracture zones, and the distribution of fractures along the well trajectory confirms previous knowledge of the area. The study also discusses the influence of factors in FIsr.

Beyond the Malleoli: Part 2—Avulsion Fractures of the Hindfoot

Avulsion fractures of the ankle and hindfoot are a diverse group of injuries occurring at level of retinacular, capsuloligamentous, and tendinous attachments. Accurate identification of avulsion fractures and their origins is based on understanding the relevant soft-tissue anatomy. In the first part of this two-series article, avulsion fractures at level of the ankle retinacula and distal tibiofibular syndesmosis were reviewed. The second part of this series of two articles is dedicated to reviewing the relevant anatomy, injury mechanisms, classification, nomenclature, imaging, treatment, and complications of hindfoot avulsion fractures occurring at level of the lateral ankle ligament complex (distally), posterior talar process, tendinous attachments, and miscellaneous soft-tissue attachments not otherwise specified.

Seismic characterization of fault and fractures in deep buried carbonate reservoirs using CNN-LSTM based deep neural networks

Geologic structures such as faults and fractures play a vital role in reservoir development studies. Fault and fracture identification requires high-quality seismic amplitude data with good resolution and a high signal-to-noise ratio (SNR). An anticline fault-controlled carbonate structure in North China contains significant reserves of hydrocarbons. However, the heterogeneity of deeply buried carbonates (burial depth > 4000 m) results in poor resolution and low SNR of seismic amplitude data, making it challenging to distinguish fault edge characteristics under the buried structures. In this study, we apply an inverse Q-filtering method based on the Gabor transform and structure-oriented filtering to compensate for the weak signal of the seismic events and enhance the SNR under the deep buried carbonate rocks. We investigated fault and adjacent fracture zones in structurally complex deeply buried carbonate reservoirs by extracting seismic fracture attributes (fault likelihood, eigenvalue coherence, and curvature) and feedforward neural network spatial fracture distribution from high-resolution seismic amplitude data. Moreover, a fracture density prediction model based on seismic attributes, CNN (convolutional neural network), and LSTM (long short-term memory) is developed to predict spatial fracture density. The predicted fracture density results are verified with seismic fracture attributes, geological modeling, and FMI (formation micro-imager) logs. This integrated workflow can assist in identifying high-production zones in structurally complex and deeply buried carbonates reservoirs.

Validation of an algorithm to identify fractures among patients within the Veterans Health Administration.

To validate an algorithm that identifies fractures using billing codes from the International Classification of Diseases Ninth Revision (ICD-9) and Tenth Revision (ICD-10) for inpatient, outpatient, and emergency department visits in a population of patients. We identified and reviewed a random sample of 543 encounters for adults receiving care within a single Veterans Health Administration healthcare system and had a first fracture episode between 2010 and 2019. To determine if an encounter represented a true incident fracture, we performed chart abstraction and assessed the type of fracture and mechanism. We calculated the positive predictive value (PPV) for the overall algorithm and each component diagnosis code along with 95% confidence intervals. Inverse probabilities of selection sampling weights were used to reflect the underlying study population. The algorithm had an initial PPV of 73.5% (confidence interval [CI] 69.5, 77.1), with low performance when weighted to reflect the full population (PPV 66.3% [CI 58.8, 73.1]). The modified algorithm was restricted to diagnosis codes with PPVs > 50% and outpatient codes were restricted to the first outpatient position, with the exception of one high performing code. The resulting unweighted PPV improved to 90.1% (CI 86.2, 93.0) and weighted PPV of 91.3% (CI 86.8, 94.4). A confirmation sample demonstrated verified performance with PPV of 87.3% (76.0, 93.7). PPVs by location of care (inpatient, emergency department and outpatient) remained greater than 85% in the modified algorithm. The modified algorithm, which included primary billing codes for inpatient, outpatient, and emergency department visits, demonstrated excellent PPV for identification of fractures among a cohort of patients within the Veterans Health Administration system.

Active identification of vertebral fracture in the FLS model of care

SummaryThe identification of vertebral fracture is a key point in an FLS. We have analyzed the characteristics of 570 patients according to the route of identification (referral by other doctors, emergency registry or through VFA), concluding that promoting referral by other doctors with a training campaign is effective.PurposeVertebral fractures (VF) are associated with increased risk of further VFs. Our objective was to analyze the characteristics of patients with VF seen in a Fracture Liaison Service (FLS).MethodsAn observational study was carried out on patients with VF referred to the outpatient metabolic clinic (OMC) after a training campaign, identified in the emergency registry, and captured by VF assessment with bone densitometry (DXA-VFA) in patients with non-VFs. Patients with traumatic VF or VF > 1 year, infiltrative or neoplastic disease were excluded. The number and severity of VFs (Genant) were analyzed. Treatment initiation in the first 6 months after baseline visit was reviewed.ResultsOverall, 570 patients were included, mean age 73. The most common route for identifying VF was through referral to OMC (303 cases), followed by the emergency registry (198) and DXA-VFA (69). Osteoporosis by DXA was found in 312 (58%) patients and 259 (45%) had ≥ 2 VFs. The rate of grade 3 VFs was highest among patients on the emergency registry. Those identified through OMC had a higher number of VFs, a higher rate of osteoporosis, more risk factors and greater treatment initiation. Patients with VFs detected by DXA-VFA were mostly women with a single VF and had a lower rate of osteoporosis by DXA.ConclusionsWe present the distribution of VFs by the route of identification in an FLS. Promoting referral by other doctors with a training campaign may help in the quality improvement of the FLS-based model of care.

Relationship of cervical soft tissue injury and surgical predication following pediatric cervical spinal trauma and its sequelae on long-term neurologic outcome

Within the sample of 181 patients with cervical CT, CT identified unstable injury with a sensitivity of 100% and specificity of 95%. CT identified operable injury at the CCJ with 86% sensitivity and 91% specificity. CT was considered the gold standard for identification of fractures. Together, the presence of CT imaging suggestive of unstable injury or persistent neurologic complaint had a 100% sensitivity and 81% specificity. Finally, across all patients MRI had 100% sensitivity and 89% specificity for detection of unstable injury requiring surgery.

NMR-Based Analysis of Fluid Occurrence Space and Imbibition Oil Recovery in Gulong Shale

The Gulong shale oil reservoir is situated in freshwater to slightly saline lacustrine basins mainly consisting of a pure shale geological structure, which is quite different from other shale reservoirs around the world. Currently, the development of Gulong shale oil mainly relies on hydraulic fracturing, while the subsequent shut-in period for imbibition has been proven to be an effective method for enhancing shale oil recovery. To clarify the characteristics of the fluid occurrence space and the variation in the fluid occurrence during saltwater imbibition in Gulong shale, this paper carried out porosity and permeability tests on Gulong shale cores and analyzed the fluid occurrence space characteristics and imbibition oil recovery based on nuclear magnetic resonance (NMR). In the porosity and permeability tests, T2 distributions were used to correct the porosity measured by the saturation method to obtain the NMR porosity. Combined with the identification of fractures in shale cores using micro-CT and the analysis of porosity and permeability parameters, it was found that the permeability of the shale cores was related to the development of fractures in the shale cores. Through the testing and analysis of T1-T2 maps of the shale cores before and after saturation with oil, it was found that the shale mainly contained heavy oil, light oil, and clay-bound water, and they were distributed in different regions in the T1-T2 maps. Finally, the T1-T2 maps of the shale cores at different imbibition stages were analyzed, and it was found that saltwater mainly entered the minuscule inorganic pores of clay minerals during the imbibition process and squeezed the larger-sized inorganic pores containing light oil through the hydration expansion effect, thus expelling the light oil from the shale core and achieving the purpose of enhanced oil recovery.