radiomic features for medical images tamper detection by equivalence checking

Abstract Digital medical images are very easy to be modified for illegal purposes. An attacker may perform this act in order to stop a political candidate, sabotage research, commit insurance fraud, perform an act of terrorism, or even commit murder. Between the machine that performs medical scans and the radiologist monitor, medical images pass through different devices: in this chain an attacker can perform its malicious action. In this paper we propose a method aimed to avoid medical images modifications by means of equivalence checking. Magnetic images are represented as finite state automata and equivalence checking is exploited to check whether the medical resource have been subject to illegal modifications

a blockchain based proposal for protecting healthcare systems through formal methods

Abstract Blockchain technology is one of the most important and disruptive technologies in the world. Multiple industries are adopting the blockchain technology to innovate the way they work. One of the industries that are looking to adopt the blockchain is the healthcare industry. In fact, the protection of the private information stored in hospital database is a critical issue. In this paper we propose a method aimed to protect information exchanged in hospital networks, with particular regard to magnetic resonance images. As required from blockchain technology, each host network must validate the transiting data network: we exploit formal equivalence checking to perform this validation, by modeling magnetic resonance images in terms of automata by exploiting radiomic features

formal modeling for magnetic resonance images tamper mitigation

Abstract The picture archiving and communication system is a medical imaging technology used primarily in healthcare organizations to store and digitally transmit electronic images and clinically-relevant reports. As demonstrated, these systems can be exploited by malicious users: in fact, considering that medical images are not digitally encrypted, any medical image modifications would be difficult to detect for a radiologist. To mitigate this aspect, in this paper a formal modelisation for picture archiving and communication system systems is proposed. The main aim is to avoid illegal writing and reading from components that should not do it, by representing the system components in terms of automa

Deep learning for heart disease detection through cardiac sounds

Abstract Most of death causes are related to cardiovascular disease. In fact, there are several anomalies afflicting the heart beat, for instance heart murmur or artefact. We propose a method for heart disease detection. By gathering a set of feature obtainable directly from cardiac sounds, we consider this feature vector as input for a deep neural network to discriminate whether a cardiac sound is belonging to an healthy or to a patient with a cardiac disease. The experiment we performed demonstrated the effectiveness of the proposed approach in real-world environment

The effect of foot position on Power Doppler Ultrasound grading of Achilles enthesitis

The aim of this study was to determine whether foot position could modify power Doppler grading in evaluation of the Achilles enthesis. Eighteen patients with clinical Achilles enthesitis were studied with power Doppler ultrasound (PDUS) in five different positions of the foot: active and passive dorsiflexion, neutral position, active and passive plantar flexion. The Doppler signal was graded in any position and compared with the others. The Doppler signal was higher with the foot in plantar flexion and decreased gradually, sometimes till to disappear, while increasing dorsiflexion. The Doppler signal was always less during the active keeping of the position of the joint, than during the passive. The PDUS examination of the Achilles enthesis should be performed also with the foot in passive plantar flexion, in order not to underestimate the degree of vascularization

CT-guided radiofrequency ablation of spinal osteoblastoma: treatment and long-term follow-up.

Osteoblastoma (OB) is a painful, rare, benign bone tumour usually observed in young populations, and this condition involves the spine in up to one-third of cases. We sought to focus on the minimally invasive treatment of spinal OB with radiofrequency ablation (RFA) under computed tomography (CT) guidance. When performed near the spinal cord, surgery can lead to instability of the spine, sometimes requiring additional interventions to stabilise the segments involved, and can cause the precocious onset of arthrosis or other degenerative diseases. The results were evaluated both clinically and with the aid of diagnostic imaging techniques during a 5-year follow-up study.Eleven patients affected by spinal OB were treated in a single session with biopsy and CT-guided RFA. Pre- and post-evaluations of the patients were performed both clinically and with CT and magnetic resonance imaging (MRI).Complete success in terms of pain relief was achieved in all patients. Additional treatments were not required in any patients. There were no complications. During follow-up, neither complications nor pathological findings related to the treatment were observed.Our experience demonstrates that RFA for spinal OB is safe and effective. One of the main advantages of this technique is represented by its lower grade of invasiveness compared with that for potentially hazardous surgical manoeuvres

Prostate Gleason Score Detection and Cancer Treatment Through Real-Time Formal Verification

Currently, there are 3.1 million American men affected by prostate cancer. Early detection represents the only way to safe lives. To evaluate a prostate cancer, the most widespread rank is the so-called Gleason score, based on the microscopic cancer appearance. Once assigned to the diagnosed prostate cancer its relative Gleason score, the correct therapy to be adopted must be promptly defined. To support pathologists and radiologists in timely diagnosis, in this paper we propose a method aimed to infer the Gleason score and the prostate cancer therapy exploiting formal methods. We consider a set of radiomic features directly obtained from magnetic resonance images. For this reason the proposed method is non invasive, since it does not require a biopsy. We model magnetic resonance images of patients as timed automata networks and we assign the Gleason score and the relative treatment, exploiting a set of temporal logic properties. In the experimental analysis, the properties are verified on 36 different patients, confirming the effectiveness of the proposed method with a sensitivity and a specificity equal to 1 for all the evaluated cases in Gleason score inference, and a sensitivity equal to 0.94 and a specificity equal to 1 in treatment prediction

Radiomics and Artificial Intelligence Can Predict Malignancy of Solitary Pulmonary Nodules in the Elderly

Solitary pulmonary nodules (SPNs) are a diagnostic and therapeutic challenge for thoracic surgeons. Although such lesions are usually benign, the risk of malignancy remains significant, particularly in elderly patients, who represent a large segment of the affected population. Surgical treatment in this subset, which usually presents several comorbidities, requires careful evaluation, especially when pre-operative biopsy is not feasible and comorbidities may jeopardize the outcome. Radiomics and artificial intelligence (AI) are progressively being applied in predicting malignancy in suspicious nodules and assisting the decision-making process. In this study, we analyzed features of the radiomic images of 71 patients with SPN aged more than 75 years (median 79, IQR 76–81) who had undergone upfront pulmonary resection based on CT and PET-CT findings. Three different machine learning algorithms were applied—functional tree, Rep Tree and J48. Histology was malignant in 64.8% of nodules and the best predictive value was achieved by the J48 model (AUC 0.9). The use of AI analysis of radiomic features may be applied to the decision-making process in elderly frail patients with suspicious SPNs to minimize the false positive rate and reduce the incidence of unnecessary surgery

Increasing differential diagnosis between lipoma and liposarcoma through radiomics: a narrative review

Soft tissue sarcomas (STSs) are rare, heterogeneous, and very often asymptomatic diseases. Their diagnosis is fundamental, as is the identification of the degree of malignancy, which may be high, medium, or low. The Italian Medical Oncology Association and European Society of Medical Oncology (ESMO) guidelines recommend magnetic resonance imaging (MRI) because the clinical examination is typically ineffective. The diagnosis of these rare diseases with artificial intelligence (AI) techniques presents reduced datasets and therefore less robust methods. However, the combination of AI techniques with radiomics may be a new angle in diagnosing rare diseases such as STSs. Results obtained are promising within the literature, not only for the performance but also for the explicability of the data. In fact, one can make tumor classification, site localization, and prediction of the risk of developing metastasis. Thanks to the synergy between computer scientists and radiologists, linking numerical features to radiological evidence with excellent performance could be a new step forward for the diagnosis of rare diseases