The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy

Funding Information: The project is funded by the European Regional Development Fund (ERDF) 1.1.1.1. project “Practical Studies”, 4th phase, project ID Nr. 1.1.1.1/20/A/035. Publisher Copyright: © 2023 by the authors.BACKGROUND: Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to the suspicious area. METHODS: A prospective single-center randomized controlled study was conducted in an outpatient endoscopy unit with the aim of evaluating the usefulness of AI-assisted colonoscopy in PDR and ADR during the day time. It is important to understand how already available CADe systems improve the detection of polyps and adenomas in order to make a decision about their routine use in practice. In the period from October 2021 to February 2022, 400 examinations (patients) were included in the study. One hundred and ninety-four patients were examined using the ENDO-AID CADe artificial intelligence device (study group), and 206 patients were examined without the artificial intelligence (control group). RESULTS: None of the analyzed indicators (PDR and ADR during morning and afternoon colonoscopies) showed differences between the study and control groups. There was an increase in PDR during afternoon colonoscopies, as well as ADR during morning and afternoon colonoscopies. CONCLUSIONS: Based on our results, the use of AI systems in colonoscopies is recommended, especially in circumstances of an increase of examinations. Additional studies with larger groups of patients at night are needed to confirm the already available data.publishersversionPeer reviewe

Comparison of nice classification for optical diagnosis of colorectal polyps and morphology of removed lesions depending on localisation in colon

Publisher Copyright: © 2022 Ilona Vilkoite et al., published by Sciendo.The narrow-band imaging (NBI) International Colorectal Endoscopic (NICE) classification is based on narrow-band pictures of colon polyps viewed through a narrow-band spectrum. The categorisation utilises staining, surface structure, and vascular patterns to differentiate between hyperplastic and adenomatous colon polyps. It is known that accuracy of the NICE classification for colorectal polyps varies depending on the localisation in the colon.The aim of this study was to compare the diagnostic accuracy of the NICE classification and the gold standard - morphological analysis for the determination of the type of colorectal lesions depending on localisation in colon. A prospective study was performed in an outpatient clinic. 1214 colonoscopies were performed by two expert endoscopists and 475 polyps were found in 291 patients. The overall diagnostic accuracy of the NICE classification was 80.3%. Optical verification was better in ascending colon - 93.9%, followed by sigmoid colon - 82.1%. Inferior results were found for the descending colon - 64.0%. The results of this study showed that the NICE classification could be a helpful instrument in daily practice for the ascending and sigmoid colon. For better results, proper training should be considered. The NICE system could have a role in the replacement of morphological analysis if appropriate results of verification could be achieved.publishersversionPeer reviewe

Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection

Funding Information: The development of the analysis approach and its evaluation and analysis were supported by a postdoctoral grant within the Activity 1.1.1.2 “Post-doctoral Research Aid” co-funded by the European Regional Development Fund (postdoctoral project numbers: 1.1.1.2/VIAA/2/18/270 and 1.1.1.2/VIAA/3/19/495). Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.publishersversionPeer reviewe

Application of sensor breath analyser for gastric cancer diagnosis

Ievads. Kuņģa vēzis ir viens no biežāk sastopamajiem un viens no nāvējošākajiem audzējiem pasaulē. Lai nodrošinātu veiksmīgu ārstēšanu, ir svarīgi kuņģa vēzi diagnosticēt agrīnās stadijās. Daudzsološa un neinvazīva kuņģa vēža skrīninga metode ir izelpas analīze, kas balstās uz gaistošo organisko savienojumu (GOS) noteikšanu izelpā, pielietojot sensorus. Darba mērķis. Izvērtēt sensoru izelpas analizatora spēju diferencēt kuņģa vēža pacientus un indivīdus bez vēža. Materiāli un metodes. Pētījumā tika iekļauti kuņģa vēža pacienti un indivīdi bez kuņģa vēža, kuri fiziski spēja ziedot savas izelpas paraugu. Izelpa tika veikta sensoru izelpas analizatorā, kurš saturēja divu veidu sensorus – astoņus zelta nanodaļiņu (GNP) sensorus un 22 metāla oksīda (MOX) sensorus. Datu statistiskā analīze tika veikta, izmantojot IBM SPSS 22.0. Darbā tika izmantotas aprakstošās un secinošās statistikas metodes. Darbā tika noteikta sensoru spēja diferencēt kuņģa vēža pacientus no indivīdiem bez kuņģa vēža. Par statistiski nozīmīgu atšķirību starp grupām tika uzskatīts, ja p<0,05. Tika novērtēta sensoru jutība, specifiskums un precizitāte, kā arī jaucējfaktoru ietekme uz sensoru darbību, kur par statistiski nozīmīgu jaucējfaktoru ietekmi tika uzskatīts, ja p<0,05. Lai noteiktu izelpas sensoru analizatora precizitāti noteikt kuņģa vēzi, tika konstruēta ROC līkne un noteikts AUC. Rezultāti. Pētījumā tika iekļauti 139 dalībnieki – 51,08% vīriešu un 48,92% sieviešu. Dalībnieki tika iedalīti divās grupās – kuņģa vēža grupa, kurā bija 55,40% dalībnieku un kontroles grupa, kurā bija 44,60% dalībnieku. Visi sensoru izelpas analizatorā izmantotie GNP sensori un 68,18% no MOX sensoriem uzrādīja statistiski nozīmīgu atšķirību starp pētījuma grupām. Tika konstatēta dzimuma, smēķēšanas statusa, alkohola lietošanas un kuņģa čūlas anamnēzē kā jaucējfaktoru ietekme uz sensoru darbību (p<0,05). Balstoties uz histoloģiskajiem datiem, tika noteikti sensoru jutīgums, specifiskums un precizitāte: GNP sensoriem – 67,53%, 90,32%, 77,70%; MOX sensoriem – 83,33%, 72,73%, 79,31%; sensoru kombinācijai – 91,67%, 95,45%, 93,10%. Secinājumi. GNP un MOX sensori spēj diferencēt kuņģa vēža pacientus no indivīdiem bez kuņģa vēža ar augstu jutīgumu, specifiskumu un precizitāti. Jaucējfaktori ietekmē GNP un MOX sensoru darbību. Atslēgvārdi. Kuņģa vēzis, gaistošie organiskie savienojumi, sensoru izelpas analizators, neinvazīvs diagnostiskais tests, kuņģa vēža skrīnings.Introduction. Gastric cancer is one of the most common and deadliest cancers worldwide. It is important to detect gastric cancer in early stages to provide successful treatment. A promising and non-invasive gastric cancer screening method is exhaled breath analysis. It is based on the detection of volatile organic compounds in exhaled breath using the sensors. Objective. To evaluate the ability of sensor breath analyser to differentiate between gastric cancer patients and subjects without cancer. Materials und methods. The study included patients with gastric cancer and subjects without gastric cancer who were physically able to perform breath test. The breath samples were collected by study subjects breathing into the aperture of the sensor breath analyser which contained two types of sensors – eight gold nanoparticle (GNP) sensors and 22 metal oxide (MOX) sensors. Statistical analysis was performed with IBM SPSS 22.0. Descriptive and inferential statistical methods were used in the study. The ability of sensors to differentiate gastric cancer patients from subjects without gastric cancer was determined. The difference was considered statistically significant at p<0,05. Sensitivity, specifity and accuracy of the sensors, as well as the influence of the confounding factors on the sensors were evaluated. Influence of the confounding factors was considered statistically significant at p<0,05. To determine the accuracy of the sensor breath analyser to detect gastric cancer, an ROC curve was constructed and AUC was determined. Results. The study group included 139 participants – 51,08% were males and 48,60% were females. Participants were divided in two groups – 55,40% gastric cancer patients and 44,60% subjects without gastric cancer. All of GNP sensors and 68,18% of MOX sensors used in the sensor breath analyser showed statistically significant difference between study groups. Gender, smoking status, alcohol consumption and gastric ulcer in medical history as the confounding factors were found to influence sensor performance (p<0,05). Based on histological data, sensitivity, specificity and accuracy of the sensors were determined: for GNP sensors – 67,53%, 90,32%, 77,70%; for MOX sensors – 83,33%, 72,73%, 79,31%; for combination of both of the sensors – 91,67%, 95,45%, 93,10%. Conclusions. GNP and MOX sensors showed the ability to differentiate gastric cancer patients from subjects without gastric cancer with high sensitivity, specificity and accuracy. The confounding factors affect GNP and MOX sensors performance. Keywords. Gastric cancer, volatile organic compounds, sensor breath analyser, non-invasive diagnostic test, gastric cancer screening