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Abstract Non-invasive brain stimulation (NIBS) techniques such as transcranial direct current stimulation (tDCS) or transcranial magnetic stimulation (TMS) have made great progress in recent years and offer boundless potential for the neuroscientific research and treatment of disorders. However, the possible use of NIBS devices for neuro-doping and neuroenhancement in healthy individuals and the military are poorly regulated. The great potentials and diverse applications can have an impact on the future development of the technology and society. This participatory study therefore aims to summarize the perspectives of different stakeholder groups with the help of qualitative workshops. Nine qualitative on-site and virtual workshops were conducted in the study with 91 individuals from seven stakeholder groups: patients, students, do-it-yourself home users of tDCS, clinical practitioners, industry representatives, philosophers, and policy experts. The co-creative and design-based workshops were tailored to each group to document the wishes, fears, and general comments of the participants. The outlooks from each group were collected in written form and summarized into different categories. The result is a comprehensive overview of the different aspects that need to be considered in the field of NIBS. For example, several groups expressed the wish for home-based tDCS under medical supervision as a potential therapeutic intervention and discussed the associated technical specifications. Other topics that were addressed were performance enhancement for certain professional groups, training requirements for practitioners, and questions of agency, among others. This qualitative participatory research highlights the potential of tDCS and repetitive TMS as alternative therapies to medication, with fewer adverse effects and home-based use for tDCS. The ethical and societal impact of the abuse of NIBS for non-clinical use must be considered for policy-making and regulation implementations. This study adds to the neuroethical debate on the responsible use and application of NIBS technologies, taking into consideration the different perspectives of important stakeholders in the field.

Abstract Previous pattern electroretinogram studies indicate reduced retinal contrast gain in patients with a major depressive disorder (MDD) which may contribute to alterations in visual perception. In line, psychophysical investigations reported elevated contrast thresholds in MDD. This study aims to gain insights into higher-level processing of visual information in MDD by evaluating contrast suppression. We examined contrast suppression of 21 MDD patients and 23 healthy controls (HC) using four different stimulus conditions (spatial frequencies 6.3 and 12.6 cpd at 30% and 60% background contrast) in a psychophysical test. Participants were instructed to adjust perceived contrasts between two vertical target patches, embedded in a horizontally or vertically oriented grid background. With finer stimulus gratings, MDD patients exhibited less contrast suppression compared to HC, particularly when the stimulus contrast was high (p = 0.006; MDD vs. HC = − 45%). Contrast suppression in the HC group was significantly reduced for the coarse compared to the fine grating, while contrast suppression scores in MDD did not change with the spatial properties of the stimulus. The reduced contrast suppression in patients with MDD supports the hypothesis of altered dopaminergic neurotransmission and could be attributed to alterations in the retinal receptive fields or in dysfunctional adaptation mechanisms in depression.

Abstract Juvenile crossbred pigs are widely used for acute and chronic animal testing due to their anatomical and physiological resemblance to humans. They are particularly prevalent in preclinical cardiovascular research, including studies investigating extracorporeal resuscitation and mechanical circulatory support devices. However, the availability of comprehensive laboratory reference values is limited. In a single-center study at the University Medical Center Freiburg, Germany, the hematologic and biochemical laboratory values of anesthetized healthy juvenile German crossbred farm pigs were determined. Blood samples were collected at the beginning of surgical procedures, either arterially or venously. Females and males were compared, and correlation with body weight was assessed. In total, 268 animals (weight 57.8 ± 12.4 kg) were included, thereof 180 castrated males (55.2 ± 7.7 kg) and 79 females (63.6 ± 18.3 kg). There were significant differences between males and females in 11 of 45 parameters and a moderate correlation between body weight and creatinine (R = 0.41, p

Abstract Large language models (LLM) have been a catalyst for the public interest in artificial intelligence (AI). These technologies perform some knowledge-based tasks better and faster than human beings. However, whether AIs can correctly assess social situations and devise socially appropriate behavior, is still unclear. We conducted an established Situational Judgment Test (SJT) with five different chatbots and compared their results with responses of human participants (N = 276). Claude, Copilot and you.com’s smart assistant performed significantly better than humans in proposing suitable behaviors in social situations. Moreover, their effectiveness rating of different behavior options aligned well with expert ratings. These results indicate that LLMs are capable of producing adept social judgments. While this constitutes an important requirement for the use as virtual social assistants, challenges and risks are still associated with their wide-spread use in social contexts.

Abstract Chloroquine and hydroxychloroquine, while effective in rheumatology, pose risks of retinal toxicity, necessitating regular screening to prevent visual disability. The gold standard for screening includes retinal imaging and automated perimetry, with multifocal electroretinography (mfERG) being a recognized but less accessible method. This study explores the efficacy of Artificial Intelligence (AI) algorithms for detecting retinal damage in patients undergoing (hydroxy-)chloroquine therapy. We analyze the mfERG data, comparing the performance of AI models that utilize raw mfERG time-series signals against models using conventional waveform parameters. Our classification models aimed to identify maculopathy, and regression models were developed to predict perimetric sensitivity. The findings reveal that while regression models were more adept at predicting non-disease-related variation, AI-based models, particularly those utilizing full mfERG traces, demonstrated superior predictive power for disease-related changes compared to linear models. This indicates a significant potential to improve diagnostic capabilities, although the unbalanced nature of the dataset may limit some applications.

Abstract Graft-versus-Host disease is a major complication of allogeneic stem cell transplantation. The eyes are a frequently affected organ with a severe dry eye disease being the hallmark manifestation. This retrospective study evaluates the effect of mini-scleral contact lenses on visual acuity, eye-related quality of life and the ocular surface. 62 eyes of 31 patients were included and visual acuity, ocular surface disease index (OSDI) questionnaire results and Oxford grades before and after mini-scleral lens fitting were compared. Median Snellen fraction with mini-scleral lenses was 20/25 (1st 20/30/3rd 20/20) compared to 20/40 median Snellen fraction with spectacles (1st 20/60/3rd 20/25). Median OSDI scores improved from 73 (1st 41.6/3rd 89) before fitting to 27 (1st 14.5/3rd 56) with mini-scleral lenses. Median Oxford grade decreased from 3 before mini-scleral lens fitting (1st 1/3rd 4.75) to 1 after mini-scleral lens fitting (1st 0/3rd 4). Median time of follow up was 717.5 days. Seven patients (22.6%) discontinued therapy with mini-scleral lenses. Mini-scleral lenses are beneficial for most patients with ocular GvHD as they improve visual acuity, eye-related quality of life and the integrity of the corneal epithelium.

Abstract Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that provides information about the Brownian motion of water molecules within biological tissues. DWI plays a crucial role in stroke imaging and oncology, but its diagnostic value can be compromised by the inherently low signal-to-noise ratio (SNR). Conventional supervised deep learning-based denoising techniques encounter challenges in this domain as they necessitate noise-free target images for training. This work presents a novel approach for denoising and evaluating DWI scans in a self-supervised manner, eliminating the need for ground-truth data. By leveraging an adapted version of Stein’s unbiased risk estimator (SURE) and exploiting a phase-corrected combination of repeated acquisitions, we outperform both state-of-the-art self-supervised denoising methods and conventional non-learning-based approaches. Additionally, we demonstrate the applicability of our proposed approach in accelerating DWI scans by acquiring fewer image repetitions. To evaluate denoising performance, we introduce a self-supervised methodology that relies on analyzing the characteristics of the residual signal removed by the denoising approaches.

Abstract Medical Object Detection (MOD) is a clinically relevant image processing method that locates structures of interest in radiological image data at object-level using bounding boxes. High-performing MOD models necessitate large datasets accurately reflecting the feature distribution of the corresponding problem domain. However, strict privacy regulations protecting patient data often hinder data consolidation, negatively affecting the performance and generalization of MOD models. Federated Learning (FL) offers a solution by enabling model training while the data remain at its original source institution. While existing FL solutions for medical image classification and segmentation demonstrate promising performance, FL for MOD remains largely unexplored. Motivated by this lack of technical solutions, we present an open-source, self-configuring and task-agnostic federated MOD framework. It integrates the FL framework Flower with nnDetection, a state-of-the-art MOD framework and provides several FL aggregation strategies. Furthermore, we evaluate model performance by creating simulated Independent Identically Distributed (IID) and non-IID scenarios, utilizing the publicly available datasets. Additionally, a detailed analysis of the distributions and characteristics of these datasets offers insights into how they can impact performance. Our framework’s implementation demonstrates the feasibility of federated self-configuring MOD in non-IID scenarios and facilitates the development of MOD models trained on large distributed databases.

Abstract Diabetic retinopathy is one of the leading causes of blindness around the world. This makes early diagnosis and treatment important in preventing vision loss in a large number of patients. Microaneurysms are the key hallmark of the early stage of the disease, non-proliferative diabetic retinopathy, and can be detected using OCT angiography quickly and non-invasively. Screening tools for non-proliferative diabetic retinopathy using OCT angiography thus have the potential to lead to improved outcomes in patients. We compared different configurations of ensembled U-nets to automatically segment microaneurysms from OCT angiography fundus projections. For this purpose, we created a new database to train and evaluate the U-nets, created by two expert graders in two stages of grading. We present the first U-net neural networks using ensembling for the detection of microaneurysms from OCT angiography en face images from the superficial and deep capillary plexuses in patients with non-proliferative diabetic retinopathy trained on a database labeled by two experts with repeats.

Abstract Glaucoma is a group of neurodegenerative diseases that can lead to irreversible blindness. Yet, the progression can be slowed down if diagnosed and treated early enough. Optical coherence tomography angiography (OCTA) can non-invasively provide valuable information about the retinal microcirculation that has shown to be correlated with the onset of the disease. The vessel density (VD) is the most commonly used biomarker to quantify this vascular information. However, different studies showed that there is a great impact of the acquisition area on the performance of the VD to distinguish between glaucoma patients and a healthy control group. It also seems that the separate capillary plexuses are differently affected by the disease and therefore also influence the results. So in this study we investigate the impact of the acquisition area (3 $$\times $$ × 3 $$\textrm{mm}$$ mm macular scan, 6.44 $$\times $$ × 6.4 $$\textrm{mm}$$ mm macular scan, 6 $$\times $$ × 6 $$\textrm{mm}$$ mm optic nerve head (ONH) scan) and the different plexuses on the machine-learning-based distinction between glaucoma patients and healthy controls. The results yielded that the 6 $$\times $$ × 6 $$\textrm{mm}$$ mm ONH show the best performance over all plexuses. Moreover the deep learning-based approach outperforms the VD as a biomarker on every acquisition area and plexus. In addition to that, it also performs better than traditional biomarkers obtained from the OCT scans that are used in the clinical routine for diagnosis and progression tracking of glaucoma. Consequently, OCTA scans of the ONH might be a useful addition to OCT when studying glaucoma.

Abstract Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP–SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP–SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP–SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a hub SNP with a significant main effect and a large minor allele frequency (MAF) tended to have a higher false-positive rate. In addition, peripheral null SNPs in a cluster with a small MAF tended to enhance false positivity. We also demonstrated that using the modified significance criterion based on the 3 p-value rules and the bootstrap approach (3pRule + bootstrap) can reduce false positivity and maintain high true positivity. In addition, our results also showed that a pair without a significant main effect tends to have weak or no interaction. This study identified the cluster effect and suggested using the 3pRule + bootstrap approach to enhance SNP–SNP interaction detection accuracy.

Abstract Virtual meetings are widespread in organizations despite being perceived as fatiguing; a phenomenon also known as Zoom fatigue. Research suggests that Zoom fatigue is stronger when the camera is on, potentially influencing individuals to conform to majority opinions during professional online meetings. Two preregistered studies were conducted to explore the relationships between camera use, Zoom fatigue, social presence, and conformity. Study 1 involved 287 participants describing a professional online meeting in terms of the content discussed as well as the study variables. Study 2 involved 64 participants in an experimentally manipulated online meeting (camera on vs. off), focusing on a personnel selection task. We measured how many times participants changed their answers to match the majority and how this was related to Zoom fatigue and self-reported conformity. Results from both studies indicated that camera use was not related to either conformity or Zoom fatigue. Despite not finding the presumed mediation effect, the studies showed a clear link between fatigue and conformity. The results explain the emergence of conformity in online meetings and provide practical information for the design of video conferences.

Abstract Dental materials are challenged by wear processes in the oral environment and should be evaluated in laboratory tests prior to clinical use. Many laboratory wear-testing devices are high-cost investments and not available for cross-centre comparisons. The ‘Rub&Roll’ wear machine enables controlled application of force, chemical and mechanical loading, but the initial design was not able to test against rigid antagonist materials. The current study aimed to probe the sensitivity of a new ‘Rub&Roll’ set-up by evaluating the effect of force and test solution parameters (deionized water; water + abrasive medium; acid + abrasive medium) on the wear behaviour of direct and indirect dental resin-based composites (RBCs) compared with human molars against 3D-printed rod antagonists. Molars exhibited greater height loss than RBCs in all test groups, with the largest differences recorded with acidic solutions. Direct RBCs showed significantly greater wear than indirect RBCs in the groups containing abrasive media. The acidic + abrasive medium did not result in increased wear of RBC materials. The developed method using the ‘Rub&Roll’ wear machine in the current investigation has provided a sensitive wear test method to allow initial screening of resin-based composite materials compared with extracted human molars under the influence of different mechanical and erosive challenges.

Abstract Accurate intraoperative assessment of organ perfusion is a pivotal determinant in preserving organ function e.g. during kidney surgery including partial nephrectomy or kidney transplantation. Hyperspectral imaging (HSI) has great potential to objectively describe and quantify this perfusion as opposed to conventional surrogate techniques such as ultrasound flowmeter, indocyanine green or the subjective eye of the surgeon. An established live porcine model under general anesthesia received median laparotomy and renal mobilization. Different scenarios that were measured using HSI were (1) complete, (2) gradual and (3) partial malperfusion. The differences in spectral reflectance as well as HSI oxygenation (StO2) between different perfusion states were compelling and as high as 56.9% with 70.3% (± 11.0%) for “physiological” vs. 13.4% (± 3.1%) for “venous congestion”. A machine learning (ML) algorithm was able to distinguish between these perfusion states with a balanced prediction accuracy of 97.8%. Data from this porcine study including 1300 recordings across 57 individuals was compared to a human dataset of 104 recordings across 17 individuals suggesting clinical transferability. Therefore, HSI is a highly promising tool for intraoperative microvascular evaluation of perfusion states with great advantages over existing surrogate techniques. Clinical trials are required to prove patient benefit.

Abstract Coagulopathy, microvascular alterations and concomitant organ dysfunctions are hallmarks of sepsis. Attempts to attenuate coagulation activation with an inhibitor of tissue factor (TF), i.e. tissue factor pathway inhibitor (TFPI), revealed no survival benefit in a heterogenous group of sepsis patients, but a potential survival benefit in patients with an international normalized ratio (INR)

Abstract Ultrasound (US) has gained popularity as a guidance modality for percutaneous needle insertions because it is widely available and non-ionizing. However, coordinating scanning and needle insertion still requires significant experience. Current assistance solutions utilize optical or electromagnetic tracking (EMT) technology directly integrated into the US device or probe. This results in specialized devices or introduces additional hardware, limiting the ergonomics of both the scanning and insertion process. We developed the first ultrasound (US) navigation solution designed to be used as a non-permanent accessory for existing US devices while maintaining the ergonomics during the scanning process. A miniaturized EMT source is reversibly attached to the US probe, temporarily creating a combined modality that provides real-time anatomical imaging and instrument tracking at the same time. Studies performed with 11 clinical operators show that the proposed navigation solution can guide needle insertions with a targeting accuracy of about 5 mm, which is comparable to existing approaches and unaffected by repeated attachment and detachment of the miniaturized tracking solution. The assistance proved particularly helpful for non-expert users and needle insertions performed outside of the US plane. The small size and reversible attachability of the proposed navigation solution promises streamlined integration into the clinical workflow and widespread access to US navigated punctures.

Abstract Fair allocation of funding in multi-centre clinical studies is challenging. Models commonly used in Germany - the case fees (“fixed-rate model”, FRM) and up-front staffing and consumables (“up-front allocation model”, UFAM) lack transparency and fail to suitably accommodate variations in centre performance. We developed a performance-based reimbursement model (PBRM) with automated calculation of conducted activities and applied it to the cohorts of the National Pandemic Cohort Network (NAPKON) within the Network of University Medicine (NUM). The study protocol activities, which were derived from data management systems, underwent validation through standardized quality checks by multiple stakeholders. The PBRM output (first funding period) was compared among centres and cohorts, and the cost-efficiency of the models was evaluated. Cases per centre varied from one to 164. The mean case reimbursement differed among the cohorts (1173.21€ [95% CI 645.68–1700.73] to 3863.43€ [95% CI 1468.89–6257.96]) and centres and mostly fell short of the expected amount. Model comparisons revealed higher cost-efficiency of the PBRM compared to FRM and UFAM, especially for low recruitment outliers. In conclusion, we have developed a reimbursement model that is transparent, accurate, and flexible. In multi-centre collaborations where heterogeneity between centres is expected, a PBRM could be used as a model to address performance discrepancies. Trial registration: https://clinicaltrials.gov/ct2/show/NCT04768998 ; https://clinicaltrials.gov/ct2/show/NCT04747366 ; https://clinicaltrials.gov/ct2/show/NCT04679584 .

Abstract Despite treatment with levothyroxine, hypothyroidism and autoimmune thyroiditis (AIT) may be associated with reduced quality of life (QoL), an enigmatic condition referred to as "syndrome T". Peripheral neuropathy, described in untreated thyroid disease, could be a contributing mechanism. We analysed autonomic and somatosensory function in 29 patients with AIT and treated hypothyroidism and 27 healthy volunteers. They underwent heart rate variability (HRV) analysis and quantitative sensory testing (n = 28), comprising 13 parameters of small and large nerve fibre function and pain thresholds. Autonomic cardiovascular function was assessed in rest, deep respiration and orthostasis. Additionally, biomarkers for autoimmunity and thyroid function were measured. Anxiety, depression and QoL were assessed using validated questionnaires. 36% of the patients showed at least one sign of somatosensory small or large fibre dysfunction. 57% presented with mild hyperalgesia to at least one stimulus. Several markers of autonomic function and some detection thresholds were related to the antibody titres. Anxiety, depression scores and QoL correlated to antibody titres and HRV measures. Autonomic and somatosensory dysfunction indicate that in treated hypothyroidism and AIT a subgroup of patients suffers from neuropathic symptoms leading to impaired QoL. Additionally, mild hyperalgesia as a possible sensitisation phenomenon should be considered a target for symptomatic treatment.

Abstract Awareness is vital for cancer prevention. US studies show a strong link between web searches and cancer incidence. In Europe, the relationship remains unclear. This study characterizes regional and temporal relationships between cancer incidence and web searches and investigates the content of searches related to breast, cervical, colorectal, lung, prostate, and testicular cancer, brain tumors, and melanoma in Germany (July 2018–December 2019). Aggregate data from Google Ads Keyword Planner and national cancer registry data were analyzed. Spearman’s correlation coefficient (r S ) examined associations between cancer incidence and web search, repeated measures correlation (r rm ) assessed time trends and searches were qualitatively categorized. The frequency of malignancy-related web searches correlated with cancer incidence (r S = 0.88, P = 0.007), e.g., breast cancer had more queries than the lower-incidence cervical cancer. Seasonally, incidence and searches followed similar patterns, peaking in spring and fall, except for melanoma. Correlations between entity incidence and searches (0.037 ≤ r rm ≤ 0.208) varied regionally. Keywords mainly focused on diagnosis, symptoms, and general information, with variations between entities. In Germany, web searches correlated with regional and seasonal incidence, revealing differences between North/East and South/West. These insights may help improve prevention strategies by identifying regional needs and assessing impact of awareness campaigns.

Abstract Multiple ophthalmic diseases lead to decreased capillary perfusion that can be visualized using optical coherence tomography angiography images. To quantify the decrease in perfusion, past studies have often used the vessel density, which is the percentage of vessel pixels in the image. However, this method is often not sensitive enough to detect subtle changes in early pathology. More recent methods are based on quantifying non-perfused or intercapillary areas between the vessels. These methods rely upon the accuracy of vessel segmentation, which is a challenging task and therefore a limiting factor for reliability. Intercapillary areas computed from perfusion-distance measures are less sensitive to errors in the vessel segmentation since the distance to the next vessel is only slightly changing if gaps are present in the segmentation. We present a novel method for distinguishing between glaucoma patients and healthy controls based on features computed from the probability density function of these perfusion-distance areas. The proposed approach is evaluated on different capillary plexuses and outperforms previously proposed methods that use handcrafted features for classification. Moreover the results of the proposed method are in the same range as the ones of convolutional neural networks trained on the raw input images and is therefore a computationally efficient, simple to implement and explainable alternative to deep learning-based approaches.

Abstract Vision transformers (ViTs) have revolutionized computer vision by employing self-attention instead of convolutional neural networks and demonstrated success due to their ability to capture global dependencies and remove spatial biases of locality. In medical imaging, where input data may differ in size and resolution, existing architectures require resampling or resizing during pre-processing, leading to potential spatial resolution loss and information degradation. This study proposes a co-ordinate-based embedding that encodes the geometry of medical images, capturing physical co-ordinate and resolution information without the need for resampling or resizing. The effectiveness of the proposed embedding is demonstrated through experiments with UNETR and SwinUNETR models for infarct segmentation on MRI dataset with AxTrace and AxADC contrasts. The dataset consists of 1142 training, 133 validation and 143 test subjects. Both models with the addition of co-ordinate based positional embedding achieved substantial improvements in mean Dice score by 6.5% and 7.6%. The proposed embedding showcased a statistically significant advantage p-value< 0.0001 over alternative approaches. In conclusion, the proposed co-ordinate-based pixel-wise positional embedding method offers a promising solution for Transformer-based models in medical image analysis. It effectively leverages physical co-ordinate information to enhance performance without compromising spatial resolution and provides a foundation for future advancements in positional embedding techniques for medical applications.

Abstract To facilitate a prospective estimation of the effective dose of an CT scan prior to the actual scanning in order to use sophisticated patient risk minimizing methods, a prospective spatial dose estimation and the known anatomical structures are required. To this end, a CT reconstruction method is required to reconstruct CT volumes from as few projections as possible, i.e. by using the topograms, with anatomical structures as correct as possible. In this work, an optimized CT reconstruction model based on a generative adversarial network (GAN) is proposed. The GAN is trained to reconstruct 3D volumes from an anterior-posterior and a lateral CT projection. To enhance anatomical structures, a pre-trained organ segmentation network and the 3D perceptual loss are applied during the training phase, so that the model can then generate both organ-enhanced CT volume and organ segmentation masks. The proposed method can reconstruct CT volumes with PSNR of 26.49, RMSE of 196.17, and SSIM of 0.64, compared to 26.21, 201.55 and 0.63 using the baseline method. In terms of the anatomical structure, the proposed method effectively enhances the organ shapes and boundaries and allows for a straight-forward identification of the relevant anatomical structures. We note that conventional reconstruction metrics fail to indicate the enhancement of anatomical structures. In addition to such metrics, the evaluation is expanded with assessing the organ segmentation performance. The average organ dice of the proposed method is 0.71 compared with 0.63 for the baseline model, indicating the enhancement of anatomical structures.

Abstract Accurately calling indels with next-generation sequencing (NGS) data is critical for clinical application. The precisionFDA team collaborated with the U.S. Food and Drug Administration’s (FDA’s) National Center for Toxicological Research (NCTR) and successfully completed the NCTR Indel Calling from Oncopanel Sequencing Data Challenge, to evaluate the performance of indel calling pipelines. Top performers were selected based on precision, recall, and F1-score. The performance of many other pipelines was close to the top performers, which produced a top cluster of performers. The performance was significantly higher in high confidence regions and coding regions, and significantly lower in low complexity regions. Oncopanel capture and other issues may have occurred that affected the recall rate. Indels with higher variant allele frequency (VAF) may generally be called with higher confidence. Many of the indel calling pipelines had good performance. Some of them performed generally well across all three oncopanels, while others were better for a specific oncopanel. The performance of indel calling can further be improved by restricting the calls within high confidence intervals (HCIs) and coding regions, and by excluding low complexity regions (LCR) regions. Certain VAF cut-offs could be applied according to the applications.

Abstract The feasibility of low frequency pure tone generation in the inner ear by laser-induced nonlinear optoacoustic effect at the round window was demonstrated in three human cadaveric temporal bones (TB) using an integral pulse density modulation (IPDM). Nanosecond laser pulses with a wavelength in the near-infrared (NIR) region were delivered to the round window niche by an optical fiber with two spherical lenses glued to the end and a viscous gel at the site of the laser focus. Using IPDM, acoustic tones with frequencies between 20 Hz and 1 kHz were generated in the inner ear. The sound pressures in scala tympani and vestibuli were recorded and the intracochlear pressure difference (ICPD) was used to calculate the equivalent sound pressure level (eq. dB SPL) as an equivalent for perceived loudness. The results demonstrate that the optoacoustic effect produced sound pressure levels ranging from 140 eq. dB SPL at low frequencies ≤ 200 Hz to 90 eq. dB SPL at 1 kHz. Therefore, the produced sound pressure level is potentially sufficient for patients requiring acoustic low frequency stimulation. Hence, the presented method offers a potentially viable solution in the future to provide the acoustic stimulus component in combined electro-acoustic stimulation with a cochlear implant.