ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

To explore the anti-depression effect of Suanzaoren Decoction(SZRD), the regulatory effects on endogenous metabolites in the liver of rats with depression induced by chronic unpredictable mild stress(CUMS) were analyzed by using LC-MS metabolomics. The rats were randomly divided into normal control group, model group, low-dose SZRD group, high-dose SZRD group, and positive drug group. The CUMS depression model was replicated by applying a variety of stimuli, such as fasting and water deprivation, ice water swimming, hot water swimming, day and night reversal, tail clamping, and restraint for rats. Modeling and treatment were conducted for 56 days. The behavioral indexes of rats in each group, including body weight, open field test, sucrose preference test, and tail suspension test, were observed. Plasma samples and liver tissue samples were collected, and the contents of 5-hydroxytryptamine(5-HT), dopamine(DA), and norepinephrine(NE) in plasma were measured using enzyme-linked immunosorbent assay(ELISA). Meanwhile, the regulatory effects of SZRD on the liver metabolic profile of CUMS model rats were analyzed by the LC-MS metabolomics method. The results show that SZRD can significantly improve the depression-like behavior of CUMS model rats and increase the neurotransmitter levels of 5-HT, DA, and NE in plasma. A total of 24 different metabolites in the rats' liver are identified using the LC-MS metabolomics method, and SZRD can reverse 13 of these metabolites. Metabolic pathway analysis indicates that nine metabolic pathways are found to be significantly associated with depression, and in the low-dose SZRD group, four pathways can be regulated, including pentose phosphate pathway, purine metabolism, inositol phosphate metabolism, and sphingolipid metabolism. In the high-dose SZRD group, two metabolic pathways can be regulated, including sphingolipid metabolism and glycerol glycerophospholipid metabolism. Sphingolipid metabolism is a metabolic pathway that can be regulated by SZRD at different doses, so it is speculated that it may be the primary pathway through which SZRD can alleviate metabolic disturbances in the liver of CUMS model rats.
Animals ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Metabolomics ; Depression/metabolism* ; Male ; Liver/drug effects* ; Rats, Sprague-Dawley ; Antidepressive Agents/administration & dosage* ; Serotonin/blood* ; Humans ; Disease Models, Animal ; Behavior, Animal/drug effects*

This study aims to reveal the correlation between the pharmacokinetics(PK) and pharmacodynamics(PD) of multiple components in Epimedii Folium-Chuanxiong Rhizoma and clarify the pharmacodynamic material basis and mechanism of this herb pair in treating osteoarthritis. The Hulth method was used to establish the rat model of osteoarthritis and plasma was collected at various time points after drug administration. The plasma concentrations of multiple components were measured. Enzyme-linked immunosorbent assay(ELISA) was used to measure the plasma concentrations of matrix metalloproteinase(MMP)-3, MMP-13, interleukin-1β(IL-1β), nitric oxide(NO), and tumor necrosis factor-α(TNF-α) as pharmacodynamic indicators. Self-defined weighting coefficients were used to calculate the PK and PD data, and a Sigmoid E_(max) fitting model was used to evaluate the synergistic effect of the compatibility of Epimedii Folium-Chuanxiong Rhizoma. The PK-PD models for Epimedii Folium, Chuanxiong Rhizoma, and Epimedii Folium-Chuanxiong Rhizoma were E=(1.926×C~(2.652))/(0.136 6~(2.652)+C~(2.652)), E=(1.618×C~(345.2))/(0.118 4~(345.2)+C~(345.2)), and E=(2.305×C~(2.786))/(0.240 3~(2.786)+C~(2.786)), respectively. The E_(max) of Epimedii Folium-Chuanxiong Rhizoma was larger than those of the two herbal medicines alone. The EC_(50) of the herb pair was lower than the sum of Epimedii Folium and Chuanxiong Rhizoma alone. The concentrations of MMP-3, MMP-13, IL-1β, NO, and TNF-α were correlated with mass concentrations of multiple components in Epimedii Folium and Chuanxiong Rhizoma, and the compatibility was better than single use. Epimedii Folium, Chuanxiong Rhizoma, and Epimedii Folium-Chuanxiong Rhizoma may play a role in the treatment of osteoarthritis by inhibiting MMP-3, MMP-13, IL-1β, NO, and TNF-α.
Animals ; Rats ; Drugs, Chinese Herbal/pharmacology* ; Male ; Rats, Sprague-Dawley ; Osteoarthritis/metabolism* ; Epimedium/chemistry* ; Interleukin-1beta/blood* ; Tumor Necrosis Factor-alpha/blood* ; Disease Models, Animal ; Nitric Oxide/blood* ; Humans ; Rhizome/chemistry*

Objective To explore the correlation of bilateral knee joint strength asymmetry with balance,walking ability,and motor function in hemiplegic stroke patients,providing a reference for clinical assessment of stroke patients.Methods A total of 46 hemiplegic stroke patients admitted to the Rehabilitation Medicine Department of People's Hospital of Shijiazhuang from February to December 2023 were selected.According to the Berg Balance Scale(BBS)scores,patients were divided into Group A(BBS score≤20,n=23)and Group B(BBS score>20,n=23).The peak torque and differences of bilateral knee flexors and extensors were compared between two groups.Isokinetic technology was used to assess the differences in peak torque of bilateral knee joints at 60°/s and 120°/s.BBS,Functional Ambulation Classification(FAC),and Fugl-Meyer Assessment of Lower Extremity(FMA-LE)were used to evaluate patients'balance,walking ability,and lower limb motor function.The correlation between bilateral knee joint peak torque and its difference with the scores of three functional scales was analyzed.Results The peak torque of knee flexors and extensors at 60°/s in group A was significantly lower than that in group B(P<0.05).At both 60°/s and 120°/s the differences in peak torque between the healthy and affected sides of knee flexors and extensors were greater than those in group B(P<0.05).At 60°/s,the difference in peak torque of bilateral knee extensors in hemiplegic stroke patients was negatively correlated with the scores of BBS,FAC,and FMA-LE(r=-0.569,-0.582,-0.606,P<0.01),as did the knee flexors(r=-0.534,-0.386,-0.458,P<0.05).At 120°/s,similar negative correlations were observed for both knee extensors(r=-0.304,-0.304,-0.443,P<0.05)and flexors(r=-0.337,-0.349,-0.370,P<0.05).Conclusions Bilateral knee joint strength asymmetry in hemiplegic stroke patients is negatively correlated with balance and walking ability.The difference in strength between the two sides of knee joint may be one of the clinical indicators for evaluating the motor function of stroke patients.

Objective To comparatively analyze the clinical characteristics of primary bilateral macronodular adrenal hyperplasia(PBMAH)and adrenal cortisol-producing Adenoma(CPA),and enhance the understanding of two diseases.Methods The clinical data of 85 PBMAH patients(PBMAH group)and 195 CPA patients(CPA group)diagnosed at Department of Endocrinology,the First Medical Center of Chinese PLA General Hospital,from September 2014 to August 2024 were retrospectively analyzed.The demographic characteristics,comorbidities,biochemical indicators,adrenocorticotropic hormone-cortisol(ACTH-F)levels,and adrenal imaging features and treatment conditions were compared between the two groups.Results(1)General characteristics:Compared with CPA group,PBMAH group had older age at diagnosis and a higher proportion of male patients.(2)Clinical characteristics:Compared with CPA group,PBMAH group had a longer disease duration,a higher proportion of subclinical Cushing's syndrome(CS),and a higher proportion of hypertension,impaired glucose tolerance/diabetes,bone mass reduction or osteoporosis,with higher serum potassium levels,and the differences were statistically significant(P<0.01).(3)Hormone levels:Both PBMAH and CPA groups showed ACTH-F rhythm disorder,significantly increased cortisol levels and suppressed ACTH.Compared with PBMAH group,CPA group had stronger autonomous cortisol secretion ability,manifested by increased midnight serum cortisol(F0:00),16:00 serum cortisol(F16:00),24-hour urinary free cortisol(24 h UFC)levels and lower 8:00 serum ACTH(ACTH8:00)and 16:00 serum ACTH(ACTH16:00)(P<0.01).After low-dose dexamethasone suppression test(LDDST),CPA group showed lower suppression rates of ACTH and cortisol,and higher proportions of paradoxical elevation in serum cortisol and 24 h UFC compared with PBMAH(P<0.01).Conclusions PBMAH has a longer disease course and higher proportions of comorbid metabolic disorders than CPA,mostly manifested as subclinical Cushing's syndrome.CPA has stronger autonomous cortisol secretion ability,with cortisol less likely to be suppressed after LDDST and more obvious paradoxical elevation of cortisol and 24 h UFC.

Objective To characterize multimodal metabolic disorders in subclinical Cushing's syndrome(SCS)patients with different cortisol levels,providing a reference for clinical diagnosis and treatment.Methods A retrospective analysis was conducted on the clinical data of 165 SCS patients diagnosed at the First Medical Center of Chinese PLA General Hospital due to adrenal masses from January 2014 to October 2024.Using the serum cortisol levels after the midnight 1 mg dexamethasone suppression test(1 mg DST)as the cut-off point,SCS patients were divided into high-level group(1 mg DST-F>138 nmol/L,n=96)and low-level group(50 nmol/L<1 mg DST-F≤138 nmol/L,n=69).The differences in age,gender,body mass index(BMI),blood pressure,glucolipid metabolism indices,electrolytes,hormone levels,and imaging features of adrenal adenoma(such as CT values)were compared between the two groups.Multivariate linear regression was used to analyze the correlation between CT values and metabolic indices.Results Compared with low-level group,patients in high-level group were younger(54.0±11.3 vs.57.7±10.3,P=0.034),while there were no statistically significant differences in gender ratio or BMI between the two groups(P>0.05).Both groups exhibited decreased adrenocorticotropic hormone(ACTH)levels and disrupted circadian rhythm.Compared with low-level group,high-level group showed significantly higher F0:00 levels[250.00(170.07,422.53)nmol/L vs.110.00(82.74,133.90)nmol/L]and 24-hour urinary free cortisol(24 h UFC)[568.40(377.80,875.45)nmol/24 h vs.369.40(265.40,494.69)nmol/24 h](P<0.001),with no significant differences in serum F8:00,or 1 mg DST ACTH0:00 levels(P>0.05).Except for the fasting C-peptide level in the high-level group being higher than that in low-level group[(2.88±1.01)ng/ml vs.(2.46±0.78)ng/ml,P=0.024],there were no significant differences in blood pressure,blood lipids,glycated hemoglobin(HbA1c),fasting blood glucose,fasting insulin,serum electrolytes,uric acid,and other indices between the two groups(P>0.05).The CT value of adrenal adenoma during contrast-enhanced scanning was higher in high-level group[80.00(17.80,93.00)Hu vs.52.00(35.50,75.00)Hu,P=0.006]compared with low-level group.Multivariate linear regression analysis revealed that diastolic blood pressure was positively correlated with CT values of adrenal adenomas in both plain scanning(β=0.49,95%CI 0.09-0.90)and contrast-enhanced scanning(β=2.08,95%CI 0.76-3.39),while triglyceride levels were negatively correlated with plain scanning CT values(β=-5.77,95%CI-10.88--0.66).Conclusion Patients with SCS at different cortisol levels differ in age,fasting C-peptide levels,and CT values.CT values may serve as potential imaging markers to assess metabolic risk in SCS patients.

Objective To investigate the clinical characteristics of patients with clinical and subclinical Cushing's syndrome caused by primary bilateral macronodular adrenal hyperplasia(PBMAH).Methods A retrospective analysis was performed on the clinical data of 198 patients with Cushing's syndrome caused by PBMAH diagnosed in the First Medical Center of Chinese PLA General Hospital from January 2004 to October 2024.According to clinical manifestations,the patients were classified into clinical type Cushing's syndrome(n=61)and subclinical type Cushing's syndrome(n=137),and the clinical characteristics of the two types were compared.Results The mean age at diagnosis of patients with PBMAH-induced Cushing's syndrome was(53.5±10.4)years,including 118 males and 80 females,with a male-to-female ratio of 1.475:1.Compared with the subclinical type,the clinical type had a higher proportion of females,higher levels of serum cortisol,24-hour urine free cortisol(24 h UFC),and inhibited serum cortisol after low-dose dexamethasone suppression.Additionally,the clinical type had lower plasma ACTH,larger adrenal nodules and a higher risk of surgery(P<0.05)compared with those in subclinical type.The incidences of hypertension,dyslipidemia,obesity,diabetes mellitus,hypokalemia,vitamin D deficiency,osteoporosis,coronary heart disease,and cerebrovascular disease in patients with Cushing's syndrome caused by PBMAH were 87.9%,50.5%,37.1%,36.9%,27.8%,25.9%,18.7%,18.7%and 12.1%,respectively.Among them,compared with subclinical type patients,clinical type patients had higher incidence of hypokalaemia,vitamin D deficiency and osteoporosis(P<0.05),while there were no statistically significant differences in the incidences of other comorbidities between the two types(P>0.05).The results of postoperative follow-up for PBMAH patients showed that the short-term biochemical remission rate of unilateral total adrenalectomy was 41.5%(22/53)and the long-term biochemical remission rate was 32.0%(8/25).The short-term biochemical remission rate of unilateral partial(or nodular)adrenalectomy was 52.9%(9/17),and the long-term biochemical remission rate was 14.3%(1/7).All patients who underwent unilateral total adrenalectomy plus contralateral partial resection developed adrenal insufficiency(3/3),and 1 patient(1/3)relapsed 3.4 years after surgery.Conclusion Clinical and subclinical types of Cushing's syndrome caused by PBMAH have their distinct clinical characteristics.Surgery is an effective treatment for PBMAH,but a certain proportion of patients fail to achieve biochemical remission after non-bilateral total adrenalectomy.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.