Computer-assisted Medical Diagnosis Research Articles

SCSONet: spatial-channel synergistic optimization net for skin lesion segmentation

In the field of computer-assisted medical diagnosis, developing medical image segmentation models that are both accurate and capable of real-time operation under limited computational resources is crucial. Particularly for skin disease image segmentation, the construction of such lightweight models must balance computational cost and segmentation efficiency, especially in environments with limited computing power, memory, and storage. This study proposes a new lightweight network designed specifically for skin disease image segmentation, aimed at significantly reducing the number of parameters and floating-point operations while ensuring segmentation performance. The proposed ConvStem module, with full-dimensional attention, learns complementary attention weights across all four dimensions of the convolution kernel, effectively enhancing the recognition of irregularly shaped lesion areas, reducing the model’s parameter count and computational burden, thus promoting model lightweighting and performance improvement. The SCF Block reduces feature redundancy through spatial and channel feature fusion, significantly lowering parameter count while improving segmentation results. This paper validates the effectiveness and robustness of the proposed SCSONet on two public skin lesion segmentation datasets, demonstrating its low computational resource requirements. https://github.com/Haoyu1Chen/SCSONet.

Innovative Deep Learning Methods for Precancerous Lesion Detection

With the continuous advancement of socio-economic levels and relentless innovation in modern medical technologies, there's been a significant increase in the importance people place on their physiological health, particularly in the context of colorectal cancer—a prevalent malignant tumor that has captivated widespread attention within the medical community for its prevention and treatment. Notably, colorectal polyps, identified as precursors to colorectal cancer, are crucial for early diagnosis and precise detection, serving as fundamental elements in averting the disease and diminishing both its incidence and mortality rates. The swift progression of deep neural network technology in recent years has revolutionized computer-assisted medical diagnosis, especially for the detection of colorectal polyps. Deep learning technology, with its robust capability for feature learning and representation, has emerged as an invaluable aid for physicians, markedly enhancing diagnostic accuracy and efficiency. This study centers on colorectal polyps, striving to develop a detection model with superior accuracy by meticulously analyzing contemporary leading target detection algorithms. By fully exploiting the potent capabilities of deep neural networks, the model aims to boost the precision of colorectal polyp detection significantly, aiding physicians in elevating detection efficiency and simplifying diagnostic processes. By undertaking this research, we aim to make a significant contribution toward more accurate and efficient technological support for the early diagnosis and prevention of colorectal polyps, thereby aiding in the reduction of both the incidence and mortality rates associated with colorectal cancer.

The “Electronic Pediatrician (EPed®)” – A clinically tested prototype software for computer-assisted pathophysiologic diagnosis and treatment of ill children

BackgroundKnowledge-based systems (KBS) are software applications based on a knowledge base and an inference engine. Various experimental KBS for computer-assisted medical diagnosis (CAMD) and treatment were started to be used since 70 s (VisualDx, GIDEON, DXPlain, CADUCEUS, Internist-I, Mycin etc). ObjectivesThis article briefly presents and tests the “Electronic Pediatrician (EPed)”, a prototype medical user-interface-based stand-alone KBS software created by the author (in Romanian language) that offers both a computer-assisted pathophysiologic diagnosis and treatment of ill children. EPed currently covers the most frequent (and mainly infectious) respiratory, digestive, renal and central nervous system pediatric diseases treated in the Department of Pediatric Infectious Diseases (DPID) of the County Emergency Hospital of Târgoviște town (Dâmbovița County, Romania). MethodsEPed uses a new type of pathophysiologically-based diagnosis algorithm, which combines the relative incidences of diseases and the degree of covering any clinical picture of any patient. EPed was tested by a retrospective study in which the author has chosen 34 clinical child cases diagnosed and treated in DPID in the period of pandemics. The diagnoses predicted by EPed for each tested patient in part was compared to the diagnoses established by the clinician for each patient, so that to test the diagnostic accuracy of EPed. ResultsThe real diagnoses of the 34 children tested were found to occupy an average list-index of 12 (varying from 1 to 57) in the lists of EPed-predicted diagnoses for each case in part (based on the clinical and paraclinical signs inputs), which is considered an acceptable result for a demo version. ConclusionsThe diagnostic accuracy of EPed was acceptable and is perfectible in the future by using other formulas of scoring which combine the relative incidences of diseases and the degree of covering the clinical picture of a patient. EPed is the first pathophysiologically-based KBS focused on general pediatrics (mainly on pediatric infectious diseases) written in Romanian.

A mask-guided attention deep learning model for COVID-19 diagnosis based on an integrated CT scan images database

The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images. The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task (without MGA module) baseline and state-of-the-art models, as measured by various popular metrics.

Does Gastroenterology Need Artificial Intelligence?

Aim. An outlook of trends and perspectives in gastroenterology in the age of digital healthcare.Key points. Diagnosis gradually transforms to the task of image recognition. Tuning a diagnostic algorithm (DA) necessarily requires a statistically representative training set of images. In transition towards electronic medical records (EMR), such data will be generated automatically. Advances in machine image recognition and the upcoming availability of a large amount of medical data suitable for configuring DA both pave the way towards efficient computerassisted diagnosis.Conclusion. The growing volumes of medical data enforce, and advances in machine image recognition enable, the transition towards computer-assisted medical diagnosis.

Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures.

Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genomics attempts to explore the associations between potential gene expression patterns and specific imaging phenotypes. These associations provide potential cellular pathophysiology information, allowing sampling of the lesion habitat with high spatial resolution. Glioblastoma (GB) poses spatial and temporal heterogeneous characteristics, challenging to current precise diagnosis and treatments for the disease. Imaging-genomics provides a powerful tool for non-invasive global assessment of GB and its response to treatment. Imaging-genomics also has the potential to advance our understanding of underlying cancer biology, gene alterations, and corresponding biological processes. This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management.

A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices.

In recent years, the incidence of skin cancer cases has risen, worldwide, mainly due to the prolonged exposure to harmful ultraviolet radiation. Concurrently, the computer-assisted medical diagnosis of skin cancer has undergone major advances, through an improvement in the instrument and detection technology, and the development of algorithms to process the information. Moreover, because there has been an increased need to store medical data, for monitoring, comparative and assisted-learning purposes, algorithms for data processing and storage have also become more efficient in handling the increase of data. In addition, the potential use of common mobile devices to register high-resolution images of skin lesions has also fueled the need to create real-time processing algorithms that may provide a likelihood for the development of malignancy. This last possibility allows even non-specialists to monitor and follow-up suspected skin cancer cases. In this review, we present the major steps in the pre-processing, processing and post-processing of skin lesion images, with a particular emphasis on the quantification and classification of pigmented skin lesions. We further review and outline the future challenges for the creation of minimum-feature, automated and real-time algorithms for the detection of skin cancer from images acquired via common mobile devices.

Computer-assisted medical diagnosis for rural Sub-Saharan Africa

There is a pressing need to employ information technology for rural healthcare in Sub-Saharan Africa. Computer-assisted diagnostic systems provide an opportunity to help meet the physician shortage in developing countries. A necessary concomitant action to deployment of such technology is the development of appropriate rural healthcare infrastructure. Promising strategies employing computer-assisted diagnostic software have been used successfully for more than seven years in rural India. Similar approaches present opportunities for exploration of new paradigms for rural healthcare delivery in rural Sub-Saharan Africa.

Non-rigid registration of a 3D ultrasound and a MR image data set of the female pelvic floor using a biomechanical model

BackgroundThe visual combination of different modalities is essential for many medical imaging applications in the field of Computer-Assisted medical Diagnosis (CAD) to enhance the clinical information content. Clinically, incontinence is a diagnosis with high clinical prevalence and morbidity rate. The search for a method to identify risk patients and to control the success of operations is still a challenging task. The conjunction of magnetic resonance (MR) and 3D ultrasound (US) image data sets could lead to a new clinical visual representation of the morphology as we show with corresponding data sets of the female anal canal with this paper.MethodsWe present a feasibility study for a non-rigid registration technique based on a biomechanical model for MR and US image data sets of the female anal canal as a base for a new innovative clinical visual representation.ResultsIt is shown in this case study that the internal and external sphincter region could be registered elastically and the registration partially corrects the compression induced by the ultrasound transducer, so the MR data set showing the native anatomy is used as a frame for the US data set showing the same region with higher resolution but distorted by the transducerConclusionThe morphology is of special interest in the assessment of anal incontinence and the non-rigid registration of normal clinical MR and US image data sets is a new field of the adaptation of this method incorporating the advantages of both technologies.

Robot-assisted knee endoprosthesis

With the development of powerful computer systems, computer-assisted medical diagnosis and therapy have become common over the last 10 years. Even in the surgical field, computer- and robotic-assisted techniques are becoming practical but are not yet used on a daily basis. In the orthopaedic field, computer and robotic assistance is used in planning and performing demanding three-dimensional osteotomies, setting pedicle screws in the spine and milling the femoral medullary canal in total hip replacement. This article introduces a computer- and robotic-assisted system for performing arthroplasty in total knee replacement procedures.

Users' Evaluation of the Navy Computer-Assisted Medical Diagnosis System

U.S. Navy Independent Duty Corpsmen (IDCs) aboard small ships and submarines are responsible for all clinical and related health care duties while at sea. During deployment, life-threatening illnesses sometimes require evacuation to a shore-based treatment facility. At-sea evacuations are dangerous, expensive, and may compromise the mission of the vessel. Therefore, Group Medical Officers and IDCs were trained to use the Navy Computer-Assisted Medical Diagnosis (NCAMD) system during deployment. They were then surveyed to evaluate the NCAMD system. Their responses show that NCAMD is a cost-efficient, user-friendly package. It is easy to learn, and is especially valuable for training in the diagnosis of chest and abdominal complaints. However, the delivery of patient care at sea would significantly improve if computer hardware were upgraded to current industry standards. Also, adding various computer peripheral devices, structured forms, and reference materials to the at-sea clinician's resources could enhance shipboard patient care.

Estimating the Parameters of Undamped Exponential Signals

A nonlinear eigenvalue method is proposed for estimating the parameters of undamped exponential signals when the parameters are complex-valued. Such data arise in several areas of application, including communications radio location of objects, seismic signal processing, and computer-assisted medical diagnosis. Osborne proposed a method to estimate the parameters of exponential models when the parameters are real-valued. The method is generalized to the complex-parameters case. It is shown to perform better than existing methods due to Tufts and Kumaresan and Bai, Krishnaiah, and Zhao in the sense of having lower mean-squared errors. A simulation study showed that the observed mean-squared errors are close to the Cramer-Rao lower bound for frequency estimates.

Shorter Communication: Estimating the Parameters of Undamped Exponential Signals

A nonlinear eigenvalue method is proposed for estimating the parameters of undamped exponential signals when the parameters are complex-valued. Such data arise in several areas of application, including communications radio location of objects, seismic signal processing, and computer-assisted medical diagnosis. Osborne proposed a method to estimate the parameters of exponential models when the parameters are real-valued. The method is generalized to the complex-parameters case. It is shown to perform better than existing methods due to Tufts and Kumaresan and Bai, Krishnaiah, and Zhao in the sense of having lower mean-squared errors. A simulation study showed that the observed mean-squared errors are close to the Cramer-Rao lower bound for frequency estimates.

Reference Standards for Software Evaluation

The field of automated ECG analysis was one of the earliest topics in Medical Informatics and may be regarded as a model both for computer-assisted medical diagnosis and for evaluating medical diagnostic programs. The CSE project has set reference standards of two kinds: In a broad sense, a standard how to perform a comprehensive evaluation study, in a narrow sense, standards as specific references for evaluating computer ECG programs. The evaluation methodology used within the CSE project is described as a basis for presentation of results which are published elsewhere in this issue.

CLINICAL PERFORMANCE OF COMPUTER-ASSISTED DIAGNOSTIC CONSULTATION.

Computer-assisted medical diagnosis is an exciting concept. However, before computerized diagnostic programs can be implemented on a widespread basis,

CADIAG: Approaches to computer-assisted medical diagnosis

CADIAG-1 is a medical expert system, based on a symbolic logic representation of medical relationships. Strong relationships such as confirming, excluding or obligatory occurrence are applied to confirm or exclude diagnoses. Weak relationships are represented by facultative and not confirming relationships (FN-relationships). Diagnostic hypotheses are established by systematic combination of symptoms showing FN-relationships. CADIAG-2, a medical expert system based on fuzzy set theory and fuzzy logic, allows detailed specification of medical relationships. Here the diagnostic process also provides confirmed and excluded diagnoses as well as diagnostic hypotheses. Hypotheses are calculated by considering fuzzy relationships between medical entities. 426 cases with rheumatic and 47 cases with pancreatic diseases were tested. For CADIAG-1, the overall accuracy for confirmation and hypothesis generation is calculated with 91.1% for rheumatic diseases and 100% for pancreatic diseases. CADIAG-2 reached an overall accuracy of 93.7% for rheumatic cases and 91.5% for pancreatic cases.

A Fuzzy Logical Model of Computer-Assisted Medical Diagnosis

A model of a computer-assisted diagnostic system using fuzzy subsets has been developed. The physician documents symptom—diagnosis presence relationships and symptom—diagnosis conclusiveness relationships by means of labels of the fuzzy subsets never, almost never, very very seldom, very seldom, seldom, more or less seldom, not known, more or less often, often, very often, very very often, almost always, always. Symptoms are regarded as fuzzy subsets of reference sets. The reference set includes all values the symptom may assume. The degree of membership of a value in the fuzzy subset of a symptom is calculated when the patient’s symptom pattern is available. By means of compositions of fuzzy relations, four different diagnostic indications are determined for every diagnosis under consideration: presence indication, conclusiveness indication, non-presence indication and non-symptom presence indication. By performing the diagnostic process, the system provides the physician with proven diagnoses, excluded diagnoses and diagnostic hints, including reasons for the diagnoses displayed. Proposals for further investigations may also be requested.

A Fuzzy Logical Model of Computer-Assisted Medical Diagnosis

A model of a computer-assisted diagnostic system using fuzzy subsets has been developed. The physician documents symptom—diagnosis presence relationships and symptom—diagnosis conclusiveness relationships by means of labels of the fuzzy subsets never, almost never, very very seldom, very seldom, seldom, more or less seldom, not known, more or less often, often, very often, very very often, almost always, always. Symptoms are regarded as fuzzy subsets of reference sets. The reference set includes all values the symptom may assume. The degree of membership of a value in the fuzzy subset of a symptom is calculated when the patient’s symptom pattern is available. By means of compositions of fuzzy relations, four different diagnostic indications are determined for every diagnosis under consideration: presence indication, conclusiveness indication, non-presence indication and non-symptom presence indication. By performing the diagnostic process, the system provides the physician with proven diagnoses, excluded diagnoses and diagnostic hints, including reasons for the diagnoses displayed. Proposals for further investigations may also be requested.

Computer-Aided Diagnosis with an Application to Endocrinology

An interactive system for computer-assisted medical diagnosis is described. Medical experts are provided with a framework in which to translate the knowledge on which their decisions are based. The technique is based on simulating the physician's own diagnostic process. The approach taken allows consistency and completeness checks to be made, thereby achieving a high degree of reliability. Information related to a given specialty is described in terms of disorder patterns, clinical facts, and logical relationships among the clinical data. Laboratory results are processed by a subsystem which uses physician-supplied rules to establish validity and to interpret the test data with respect to their diagnostic significance. The system incorporates a dynamically generated questionnaire which provides efficient gathering of anamnestic, observational, and other clinical parameters. The physician is prompted for relevant patient data by an algorithm which assures that an almost minimal amount of information is requested. Simulation facilities allow the user to examine clustering phenomena among disorders, and an option is included that traces the logic of any decision taken to exclude candidates for a final diagnosis.