How to support dental students in reading radiographs: effects of a gaze-based compare-and-contrast intervention.

In dental medicine, interpreting radiographs (i.e., orthopantomograms, OPTs) is an error-prone process, even in experts. Effective intervention methods are therefore needed to support students in improving their image reading skills for OPTs. To this end, we developed a compare-and-contrast intervention, which aimed at supporting students in achieving full coverage when visually inspecting OPTs and, consequently, obtaining a better diagnostic performance. The comparison entailed a static eye movement visualization (heat map) on an OPT showing full gaze coverage from a peer-model (other student) and another heat map showing a student's own gaze behavior. The intervention group (N = 38) compared five such heat map combinations, whereas the control group (N = 23) diagnosed five OPTs. Prior to the experimental variation (pre-test) and after it (post-test), students in both conditions searched for anomalies in OPTs while their gaze was recorded. Results showed that students in the intervention group covered more areas of the OPTs and looked less often and for a shorter amount of time at anomalies after the intervention. Furthermore, they fixated on low-prevalence anomalies earlier and high-prevalence anomalies later during the inspection. However, the students in the intervention group did not show any meaningful improvement in detection rate and made more false positive errors compared to the control group. Thus, the intervention guided visual attention but did not improve diagnostic performance substantially. Exploratory analyses indicated that further interventions should teach knowledge about anomalies rather than focusing on full coverage of radiographs.

Pupil diameter changes reflect difficulty and diagnostic accuracy during medical image interpretation.

BACKGROUND: No automated methods exist to objectively monitor and evaluate the diagnostic process while physicians review computerized medical images. The present study tested whether using eye tracking to monitor tonic and phasic pupil dynamics may prove valuable in tracking interpretive difficulty and predicting diagnostic accuracy. METHODS: Pathologists interpreted digitized breast biopsies varying in diagnosis and rated difficulty, while pupil diameter was monitored. Tonic diameter was recorded during the entire duration of interpretation, and phasic diameter was examined when the eyes fixated on a pre-determined diagnostic region during inspection. RESULTS: Tonic pupil diameter was higher with increasing rated difficulty levels of cases. Phasic diameter was interactively influenced by case difficulty and the eventual agreement with consensus diagnosis. More difficult cases produced increases in pupil diameter, but only when the pathologists' diagnoses were ultimately correct. All results were robust after adjusting for the potential impact of screen brightness on pupil diameter. CONCLUSIONS: Results contribute new understandings of the diagnostic process, theoretical positions regarding locus coeruleus-norepinephrine system function, and suggest novel approaches to monitoring, evaluating, and guiding medical image interpretation.

Machine learning classification of diagnostic accuracy in pathologists interpreting breast biopsies.

OBJECTIVE: This study explores the feasibility of using machine learning to predict accurate versus inaccurate diagnoses made by pathologists based on their spatiotemporal viewing behavior when evaluating digital breast biopsy images. MATERIALS AND METHODS: The study gathered data from 140 pathologists of varying experience levels who each reviewed a set of 14 digital whole slide images of breast biopsy tissue. Pathologists' viewing behavior, including zooming and panning actions, was recorded during image evaluation. A total of 30 features were extracted from the viewing behavior data, and 4 machine learning algorithms were used to build classifiers for predicting diagnostic accuracy. RESULTS: The Random Forest classifier demonstrated the best overall performance, achieving a test accuracy of 0.81 and area under the receiver-operator characteristic curve of 0.86. Features related to attention distribution and focus on critical regions of interest were found to be important predictors of diagnostic accuracy. Further including case-level and pathologist-level information incrementally improved classifier performance. DISCUSSION: Results suggest that pathologists' viewing behavior during digital image evaluation can be leveraged to predict diagnostic accuracy, affording automated feedback and decision support systems based on viewing behavior to aid in training and, ultimately, clinical practice. They also carry implications for basic research examining the interplay between perception, thought, and action in diagnostic decision-making. CONCLUSION: The classifiers developed herein have potential applications in training and clinical settings to provide timely feedback and support to pathologists during diagnostic decision-making. Further research could explore the generalizability of these findings to other medical domains and varied levels of expertise.

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer.

Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen's Kappa value. These indicators highlight the model's proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information.

Medical visual question answering: A survey.

Medical Visual Question Answering (VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer. Although the general-domain VQA has been extensively studied, the medical VQA still needs specific investigation and exploration due to its task features. In the first part of this survey, we collect and discuss the publicly available medical VQA datasets up-to-date about the data source, data quantity, and task feature. In the second part, we review the approaches used in medical VQA tasks. We summarize and discuss their techniques, innovations, and potential improvements. In the last part, we analyze some medical-specific challenges for the field and discuss future research directions. Our goal is to provide comprehensive and helpful information for researchers interested in the medical visual question answering field and encourage them to conduct further research in this field.

Prepubescent Female Genital Examination Images: Evidence-Informed Learning Opportunities.

OBJECTIVES: To determine diagnoses and image features that are associated with difficult prepubescent female genital image interpretations. DESIGN AND SETTING: This was a mixed-methods study conducted at a tertiary care pediatric center using images from a previously developed education platform. PARTICIPANTS: Participants comprised 107 medical students, residents, fellows, and attendings who interpreted 158 cases to derive case difficulty estimates. INTERVENTIONS: This was a planned secondary analysis of participant performance data obtained from a prospective multi-center cross-sectional study. An expert panel also performed a descriptive review of images with the highest frequency of diagnostic error. MAIN OUTCOME MEASURES: We derived the proportion of participants who interpreted an image correctly, and features that were common in images with the most frequent diagnostic errors. RESULTS: We obtained 16,906 image interpretations. The mean proportion correct scores for each diagnosis were as follows: normal/normal variants 0.84 (95% confidence interval [CI] 0.82, 0.87); infectious/dermatology pathology 0.59 (95% CI 0.45, 0.73); anatomic pathology 0.61 (95% CI 0.41, 0.81); and, traumatic pathology 0.64 (95% CI 0.49, 0.79). The mean proportion correct scores varied by diagnosis (P < .001). The descriptive review demonstrated that poor image quality, infant genitalia, normal variant anatomy, external material (eg, diaper cream) in the genital area, and nonspecific erythema were common features in images with lower accuracy scores. CONCLUSIONS: A quantitative and qualitative examination of prepubescent female genital examination image interpretations provided insight into diagnostic challenges for this complex examination. These data can be used to inform the design of teaching interventions to improve skill in this area.

Eye tracking reveals expertise-related differences in the time-course of medical image inspection and diagnosis.

Purpose: Physicians' eye movements provide insights into relative reliance on different visual features during medical image review and diagnosis. Current theories posit that increasing expertise is associated with relatively holistic viewing strategies activated early in the image viewing experience. This study examined whether early image viewing behavior is associated with experience level and diagnostic accuracy when pathologists and trainees interpreted breast biopsies. Approach: Ninety-two residents in training and experienced pathologists at nine major U.S. medical centers interpreted digitized whole slide images of breast biopsy cases while eye movements were monitored. The breadth of visual attention and frequency and duration of eye fixations on critical image regions were recorded. We dissociated eye movements occurring early during initial viewing (prior to first zoom) versus later viewing, examining seven viewing behaviors of interest. Results: Residents and faculty pathologists were similarly likely to detect critical image regions during early image viewing, but faculty members showed more and longer duration eye fixations in these regions. Among pathology residents, year of residency predicted increasingly higher odds of fixating on critical image regions during early viewing. No viewing behavior was significantly associated with diagnostic accuracy. Conclusions: Results suggest early detection and recognition of critical image features by experienced pathologists, with relatively directed and efficient search behavior. The results also suggest that the immediate distribution of eye movements over medical images warrants further exploration as a potential metric for the objective monitoring and evaluation of progress during medical training.

Validation of a Novel Cutaneous Neoplasm Diagnostic Self-Efficacy Instrument (CNDSEI) for Evaluating User-Perceived Confidence With Dermoscopy.

BACKGROUND: Accurate medical image interpretation is an essential proficiency for multiple medical specialties, including dermatologists and primary care providers. A dermatoscope, a ×10-×20 magnifying lens paired with a light source, enables enhanced visualization of skin cancer structures beyond standard visual inspection. Skilled interpretation of dermoscopic images improves diagnostic accuracy for skin cancer. OBJECTIVE: Design and validation of Cutaneous Neoplasm Diagnostic Self-Efficacy Instrument (CNDSEI)-a new tool to assess dermatology residents' confidence in dermoscopic diagnosis of skin tumors. METHODS: In the 2018-2019 academic year, the authors administered the CNDSEI and the Long Dermoscopy Assessment (LDA), to measure dermoscopic image interpretation accuracy, to residents in 9 dermatology residency programs prior to dermoscopy educational intervention exposure. The authors conducted CNDSEI item analysis with inspection of response distribution histograms, assessed internal reliability using Cronbach's coefficient alpha (α) and construct validity by comparing baseline CNDSEI and LDA results for corresponding lesions with one-way analysis of variance (ANOVA). RESULTS: At baseline, residents respectively demonstrated significantly higher and lower CNDSEI scores for correctly and incorrectly diagnosed lesions on the LDA (P = 0.001). The internal consistency reliability of CNDSEI responses for the majority (13/15) of the lesion types was excellent (α ≥ 0.9) or good (0.8≥ α <0.9). CONCLUSIONS: The CNDSEI pilot established that the tool reliably measures user dermoscopic image interpretation confidence and that self-efficacy correlates with diagnostic accuracy. Precise alignment of medical image diagnostic performance and the self-efficacy instrument content offers opportunity for construct validation of novel medical image interpretation self-efficacy instruments.

What We Do and Do Not Know about Teaching Medical Image Interpretation.

Educators in medical image interpretation have difficulty finding scientific evidence as to how they should design their instruction. We review and comment on 81 papers that investigated instructional design in medical image interpretation. We distinguish between studies that evaluated complete offline courses and curricula, studies that evaluated e-learning modules, and studies that evaluated specific educational interventions. Twenty-three percent of all studies evaluated the implementation of complete courses or curricula, and 44% of the studies evaluated the implementation of e-learning modules. We argue that these studies have encouraging results but provide little information for educators: too many differences exist between conditions to unambiguously attribute the learning effects to specific instructional techniques. Moreover, concepts are not uniformly defined and methodological weaknesses further limit the usefulness of evidence provided by these studies. Thirty-two percent of the studies evaluated a specific interventional technique. We discuss three theoretical frameworks that informed these studies: diagnostic reasoning, cognitive schemas and study strategies. Research on diagnostic reasoning suggests teaching students to start with non-analytic reasoning and subsequently applying analytic reasoning, but little is known on how to train non-analytic reasoning. Research on cognitive schemas investigated activities that help the development of appropriate cognitive schemas. Finally, research on study strategies supports the effectiveness of practice testing, but more study strategies could be applicable to learning medical image interpretation. Our commentary highlights the value of evaluating specific instructional techniques, but further evidence is required to optimally inform educators in medical image interpretation.

Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms: A comparative analysis.

Female breast cancer is the second most common cancer in the world. Several efforts in artificial intelligence have been made to help improving the diagnostic accuracy at earlier stages. However, the identification of breast abnormalities, like masses, on mammographic images is not a trivial task, especially for dense breasts. In this paper we describe our novel mass detection process that includes three successive steps of enhancement, characterization and classification. The proposed enhancement system is based mainly on the analysis of the breast texture. First of all, a filtering step with morphological operators and soft thresholding is achieved. Then, we remove from the filtered breast region, all the details that may interfere with the eventual masses, including pectoral muscle and galactophorous tree. The pixels belonging to this tree will be interpolated and replaced by the average of the neighborhood. In the characterization process, measurement of the Gaussian density in the wavelet domain allows the segmentation of the masses. Finally, a comparative classification mechanism based on the Bayesian regularization back-propagation networks and ANFIS techniques is proposed. The tests were conducted on the MIAS database. The results showed the robustness of the proposed enhancement method.