Die Konservierung von Boden- und Wasserressourcen in den nördlichen Anden Perus

This thesis investigates hydro-meteorological boundary conditions in the region of Cajamarca in the northern Andes of Peru and quantifies the impact of selected resource conservation measures on the hydrology of the Ronquillo watershed. The research was undertaken as part of the research project The conservation of water and soil resources in the Chetillano and Ronquillo basins in the Northern Sierra of Peru (CASCUS). The project aims to identify opportunities for enhancing water availability and reducing soil erosion in the region of Cajamarca. This thesis aims to contribute to the CASCUS project by strengthening the knowledge base on hydro-meteorological boundary conditions in the research area. It also seeks to advance the envisioned integrated resource management strategy by quantifying the impact of selected resource conservation measures on the hydrology of the Ronquillo watershed. In order to achieve these objectives, research was undertaken in several stages. Specifically, these stages were: (1) the exploration of the meteorological and hydrological boundary conditions, (2) the development of scenarios for the implementation of resource conservation measures, and (3) the assessment of the hydrological impact of resource conservation measures on the catchment by applying a rainfall-runoff model. The region under investigation is characterized by a complex mountain climate marked by the interaction of a number of meteorological features, including seasonal displacement of the Intertropical Convergence Zone, orographic rainout, rain-bearing mesoscale cloud systems, El Niño Southern Oscillation (ENSO), katabatic drainage flow and local convection, all of which act on different spatial and temporal scales. The Ronquillo watershed displays strong seasonality in stream flow. During the rainy season a large portion of stream flow originates from direct runoff, which drains the watershed rapidly. The main flood formation areas are located in the middle part of the catchment, where soil and land coverage characteristics are most prone to generate surface runoff during strong rainfall. During the dry season a large portion of the discharge of the Ronquillo River originates from the soils covering the high altitude Jalca grasslands. In addition the basement rock aquifers and spring discharge significantly contribute to the dry seasonal discharge. The main objective of the scenario development phase is to provide implementation scenarios for selected soil and water conservation techniques (SWCTs) in the Ronquillo watershed, in order to evaluate their impact on catchment hydrology by applying a rainfall-runoff model. The present study evaluates various implementation scenarios for SWCTs, which fall under the categories “earthworks,” “afforestation,” and “check dam construction.” The earthworks scenarios are developed on the basis of a decision support model. Therefore a multi-criteria evaluation procedure is used that takes into account environmental site assessment criteria such as meteorology, hydrology, topography, land use, and soil properties. Each environmental site assessment criterion is evaluated using a pair-wise comparison matrix method, known as the Analytical Hierarchy Process. Afforestation scenarios are developed for the planting of pine and eucalyptus species, based on the underlying hypotheses that existing tree coverage areas can build the nucleus for future afforestation and that primarily degraded areas will be subject to afforestation. For the implementation of check dams, two scenarios are developed. In the first, check dams are implemented in all stream channels, whereas in the second, check dams are implemented in intermittent stream channels only. The impact of different SWCTs on the hydrology of the Ronquillo watershed is assessed using a hydrological modeling approach. Analysis undertaken with the NASIM rainfall-runoff model shows that earthworks (terraces and bund systems) and afforestation scenarios considerably impact the hydrology of the Ronquillo River. By contrast, the impact of check dam scenarios on catchment hydrology is comparatively small. The results imply that earthworks and afforestation reduce surface runoff, and thus mitigate the self-reinforcing process of surface runoff generation and subsequent soil erosion. However, this comes at the expense of a reduction in stream flow. On- site effects such as the reduction of overland flow and enhanced water availability for crop growth in situ are counterbalanced by a reduction in water availability off-site. The modeling results imply that within the framework of a water resource conservation strategy, the implementation of earthworks compared to afforestation measures is preferable, as earthworks reduce surface runoff more efficiently compared to afforestation measures, and thus have less impact on water availability downstream.Die Dissertation quantifiziert den Einfluss ressourcenkonservierender Maßnahmen auf die Hydrologie des Einzugsgebietes des Ronquillo in der Region Cajamarca in den nördlichen Anden Perus. Das Vorhaben ist Teil des Forschungsprojektes Konservierung der Wasser- und Bodenressourcen in den Einzugsgebieten des Chetillano und Ronquillo in der nördlichen Sierra Perus (CASCUS). Die Ziele des Projektes CASCUS sind der dezentrale Wasserrückhalt und Bodenschutz durch boden- und wasserkonservierende Maßnahmen in der Region Cajamarca. Diese Maßnahmen sollen den Oberflächenabfluss und die Bodenerosion verringern, Hochwasserspitzen reduzieren und das Grundwasservolumen als Wasserspeicher für Trockenperioden erhöhen. Die Arbeit ist in folgende, aufeinander aufbauende Themenkomplexe strukturiert: Zu Beginn steht die Analyse der hydrometeorologischen Randbedingungen. Darauf folgt die Entwicklung von Implementierungsszenarien ressourcenkonservierender Maßnahmen. Schließlich bietet die Forschungsarbeit eine Quantifizierung ihrer Wirkung auf die Hydrologie des Einzugsgebietes des Ronquillo durch Anwendung des flächendetaillierten hydrologischen Modells NASIM (Niederschlag-Abfluss- Simulationsmodell). Die Region Cajamarca ist durch ein Tageszeiten- und Gebirgsklima gekennzeichnet. Hier wirken meteorologische Phänomene auf unterschiedlichen räumlichen und zeitlichen Skalen zusammen; z.B. die jahreszeitliche Wanderung der äquatorialen Tiefdruckrinne, El Niño Southern Oscillation (ENSO), große Gewitter- und Schauergebiete (Mesoscale Convective Systems), orographischer Niederschlag, lokale Konvektion und ein ausgeprägtes Hang- und Talwindsystem. Der Abfluss des Ronquillo ist aufgrund der hydrometeorologischen Randbedingungen stark saisonal geprägt. Ein großer Teil des Abflusses in der Regenzeit ist dem Direktabfluss (Oberflächenabfluss und schneller Zwischenabfluss) zuzuordnen. Der trockenzeitliche Abfluss wird zu einem wesentlichen Teil von den Böden der Jalca Höhenstufe (> 3500 m ü.M.) generiert. Weiter tragen lokale Aquifere und Quellschüttungen zum trockenzeitlichen Abfluss bei. Die untersuchten boden- und wasserkonservierenden Maßnahmen gliedern sich in Maßnahmen in der Fläche: Terrassen- und Erdwallsysteme (earthworks) sowie Aufforstung; und Maßnahmen im Fließgewässer: die Errichtung von kleinen Rückhaltedämmen im Gerinne (check dams). Die Implementierungsszenarien für Terrassen und Erdwälle basieren auf einer multikriteriellen räumlichen Eignungsklassifizierung. Die Ausweisung geeigneter Flächen für Terrassen und Erdwälle erfolgt in Abhängigkeit von meteorologischen, hydrologischen und topographischen Eignungskriterien sowie der Landnutzung/-bedeckung und der Bodeneigenschaften. Aufforstungsszenarien werden für die in der Region Cajamarca weit verbreiteten Kiefern- und Eukalyptusbestände entwickelt. Die grundlegende Idee hierbei ist, dass die bereits existierenden Aufforstungsflächen die Keimzelle für weitere Aufforstungen darstellen und, dass vorrangig stark degradierte Flächen aufgeforstet werden. Für die Errichtung von Rückhaltedämmen werden zwei Szenarien entwickelt: der Einbau von Rückhaltedämmen in allen Gerinneabschnitten und der Einbau von Rückhaltedämmen in nur periodisch durchflossenen Gerinneabschnitten. Das Niederschlag-Abfluss-Modell NASIM wird szenarienbasiert eingesetzt, um den Einfluss der untersuchten boden- und wasserkonservierenden Maßnahmen auf die Einzugs-gebietshydrologie des Ronquillo zu quantifizieren. Die Modellierungsergebnisse zeigen, dass Terrassen, Erdwälle und Aufforstungen einen erheblichen Einfluss auf die Hydrologie des Ronquillo haben. Der Einfluss von Rückhaltedämmen ist hingegen von untergeordneter Bedeutung. Die Maßnahmen in der Fläche verringern die Oberflächenabflussbildung und reduzieren die Hochwasserspitzen; aber gleichzeitig nimmt die Abflussmenge ab. Die Aufforstung mit Kiefern- und Eukalyptusbeständen erhöht gegenüber der natürlichen Vegetation oder der landwirtschaftlichen Nutzung die Interzeptionsverdunstung und die Transpiration. Die höhere Transpirationsleistung der Aufforstungsflächen drückt sich in der Änderung der Bodenfeuchte und der Verringerung der Abflussmenge flussabwärts aus. Ein verminderter Bestandsniederschlag und eine geringere Bodenvorfeuchte reduzieren die Oberflächenabflussbildung und auch die Hochwasserscheitel. Die infiltrationsfördernden Terrassen und Erdwälle erhöhen lokal die Wasserverfügbarkeit und verringern sowohl den Oberflächenabfluss als auch die Abflussmaxima. Das im Boden gespeicherte Wasser wird aber aufgrund der hohen potentiellen Evapotranspirationsraten nicht abflusswirksam, sondern verdunstet oder transpiriert. Die Ergebnisse zeigen, dass landwirtschaftlich genutzte Böden im Einzugsgebiet des Ronquillo nicht geeignet sind, Wasser über längere Zeiträume zwischen zu speichern, um die Wasserverfügbarkeit in den Trockenperioden zu erhöhen. Desweiteren zeigt sich, dass Terrassen und Erdwälle den Oberflächenabfluss effizienter reduzieren als Aufforstungen, insofern als dass sie die Wasserverfügbarkeit flussabwärts nur in vergleichsweise geringerem Maße reduzieren

Deep Learning for the Radiographic Detection of periodontal Bone Loss

We applied deep convolutional neural networks (CNNs) to detect periodontal bone loss (PBL) on panoramic dental radiographs. We synthesized a set of 2001 image segments from panoramic radiographs. Our reference test was the measured % of PBL. A deep feed-forward CNN was trained and validated via 10-times repeated group shuffling. Model architectures and hyperparameters were tuned using grid search. The final model was a seven-layer deep neural network, parameterized by a total number of 4,299,651 weights. For comparison, six dentists assessed the image segments for PBL. Averaged over 10 validation folds the mean (SD) classification accuracy of the CNN was 0.81 (0.02). Mean (SD) sensitivity and specificity were 0.81 (0.04), 0.81 (0.05), respectively. The mean (SD) accuracy of the dentists was 0.76 (0.06), but the CNN was not statistically significant superior compared to the examiners (p = 0.067/t-test). Mean sensitivity and specificity of the dentists was 0.92 (0.02) and 0.63 (0.14), respectively. A CNN trained on a limited amount of radiographic image segments showed at least similar discrimination ability as dentists for assessing PBL on panoramic radiographs. Dentists’ diagnostic efforts when using radiographs may be reduced by applying machine-learning based technologies

Evaluation of the Clinical, Technical, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis

Background: Cost-effectiveness analysis of artificial intelligence (AI) in medicine demands consideration of clinical, technical, and economic aspects to generate impactful research of a novel and highly versatile technology. Objective: We aimed to systematically scope existing literature on the cost-effectiveness of AI and to extract and summarize clinical, technical, and economic dimensions required for a comprehensive assessment. Methods: A scoping literature review was conducted to map medical, technical, and economic aspects considered in studies on the cost-effectiveness of medical AI. Based on these, a framework for health policy analysis was developed. Results: Among 4820 eligible studies, 13 met the inclusion criteria for our review. Internal medicine and emergency medicine were the clinical disciplines most frequently analyzed. Most of the studies included were from the United States (5/13, 39%), assessed solutions requiring market access (9/13, 69%), and proposed optimization of direct resources as the most frequent value proposition (7/13, 53%). On the other hand, technical aspects were not uniformly disclosed in the studies we analyzed. A minority of articles explicitly stated the payment mechanism assumed (5/13, 38%), while it remained unspecified in the majority (8/13, 62%) of studies. Conclusions: Current studies on the cost-effectiveness of AI do not allow to determine if the investigated AI solutions are clinically, technically, and economically viable. Further research and improved reporting on these dimensions seem relevant to recommend and assess potential use cases for this technology

Outcome and comparator choice in molar incisor hypomineralisation (MIH) intervention studies: a systematic review and social network analysis

OBJECTIVES: Outcome and comparator choice strongly determine the validity and implementation of clinical trial results. We aimed to assess outcome and comparator choice in intervention studies on molar incisor hypomineralisation (MIH) using systematic review and social network analysis (SNA). DESIGN AND DATA SOURCES: Medline, Embase, Cochrane Central, Google Scholar, opengrey.eu as well as DRKS.de and Clinicaltrials.gov were searched for MIH intervention studies. The search covered the period from 1980 to 2019. ELIGIBILITY CRITERIA: Clinical single-arm/multiarm, controlled/uncontrolled studies reporting on the management of MIH were included. Reported outcomes and comparators were extracted and categorised. SNA was used to evaluate comparator choice and the resulting trial networks. DATA EXTRACTION: Of the 7979 identified records, 100 were evaluated in full text and 35 studies (17 randomised controlled trials, 14 prospective and 4 retrospective cohort studies) were included. RESULTS: In total, 2124 patients with a mean age of 11 years (min/max 6/70 years) were included. Outcomes fell in one of 11 different outcome categories: restoration success, aesthetic improvement, pain/hypersensitivity/discomfort, mineral gain, space management, anaesthesia effectiveness, preventive success, efficiency, quality of life, gingival and periodontal health and patient satisfaction. Comparators were mainly restorative interventions (17 studies), remineralisation (3), treatment of hypersensitivity (10), aesthetic interventions (5) and orthodontic interventions (3). Two highly clustered comparator networks emerged; many interventions were not robustly linked to these networks. CONCLUSIONS: MIH intervention studies recorded both clinically centred and patient-centred outcomes. Core outcome set development should consider these and supplement them with outcomes on, for example, applicability. The high number of compared interventions tested in only few studies and our SNA results implicate that the current evidence may not be robust

Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and Diabetic Retinopathy

Objective: To assess the cost-effectiveness of artificial intelligence (AI) for supporting clinicians in detecting and grading diseases in dermatology, dentistry, and ophthalmology. Importance: AI has been referred to as a facilitator for more precise, personalized, and safer health care, and AI algorithms have been reported to have diagnostic accuracies at or above the average physician in dermatology, dentistry, and ophthalmology. Design, setting, and participants: This economic evaluation analyzed data from 3 Markov models used in previous cost-effectiveness studies that were adapted to compare AI vs standard of care to detect melanoma on skin photographs, dental caries on radiographs, and diabetic retinopathy on retina fundus imaging. The general US and German population aged 50 and 12 years, respectively, as well as individuals with diabetes in Brazil aged 40 years were modeled over their lifetime. Monte Carlo microsimulations and sensitivity analyses were used to capture lifetime efficacy and costs. An annual cycle length was chosen. Data were analyzed between February 2021 and August 2021. Exposure: AI vs standard of care. Main outcomes and measures: Association of AI with tooth retention-years for dentistry and quality-adjusted life-years (QALYs) for individuals in dermatology and ophthalmology; diagnostic costs. Results: In 1000 microsimulations with 1000 random samples, AI as a diagnostic-support system showed limited cost-savings and gains in tooth retention-years and QALYs. In dermatology, AI showed mean costs of $750 (95$608-$970) and was associated with 86.5 QALYs (95$759 (95% CI, $618-$970) with similar QALY outcome. In dentistry, AI accumulated costs of €320 (95% CI, €299-€341) (purchasing power parity [PPP] conversion, $429 [95$400-$458]) with 62.4 years per tooth retention (95$458; 95% CI, $426-$493) and fewer tooth retention-years (60.9 years; 95% CI, 60.5-63.1 years). In ophthalmology, AI accrued costs of R $1321 (95$1283-R $1364) (PPP,$559; 95% CI, $543-$577) at 8.4 QALYs (95% CI, 8.0-8.7 QALYs), while the control was less expensive (R $1260; 95$1222-R $1303) (PPP,$533; 95% CI, $517-$551) and associated with similar QALYs. Dominance in favor of AI was dependent on small differences in the fee paid for the service and the treatment assumed after diagnosis. The fee paid for AI was a factor in patient preferences in cost-effectiveness between strategies. Conclusions and relevance: The findings of this study suggest that marginal improvements in diagnostic accuracy when using AI may translate into a marginal improvement in outcomes. The current evidence supporting AI as decision support from a cost-effectiveness perspective is limited; AI should be evaluated on a case-specific basis to capture not only differences in costs and payment mechanisms but also treatment after diagnosis

Predicting mortality in the very old: a machine learning analysis on claims data.

Machine learning (ML) may be used to predict mortality. We used claims data from one large German insurer to develop and test differently complex ML prediction models, comparing them for their (balanced) accuracy, but also the importance of different predictors, the relevance of the follow-up period before death (i.e. the amount of accumulated data) and the time distance of the data used for prediction and death. A sample of 373,077 insured very old, aged 75 years or above, living in the Northeast of Germany in 2012 was drawn and followed over 6 years. Our outcome was whether an individual died in one of the years of interest (2013-2017) or not; the primary metric was (balanced) accuracy in a hold-out test dataset. From the 86,326 potential variables, we used the 30 most important ones for modeling. We trained a total of 45 model combinations: (1) Three different ML models were used; logistic regression (LR), random forest (RF), extreme gradient boosting (XGB); (2) Different periods of follow-up were employed for training; 1-5 years; (3) Different time distances between data used for prediction and the time of the event (death/survival) were set; 0-4 years. The mortality rate was 9.15% in mean per year. The models showed (balanced) accuracy between 65 and 93%. A longer follow-up period showed limited to no advantage, but models with short time distance from the event were more accurate than models trained on more distant data. RF and XGB were more accurate than LR. For RF and XGB sensitivity and specificity were similar, while for LR sensitivity was significantly lower than specificity. For all three models, the positive-predictive-value was below 62% (and even dropped to below 20% for longer time distances from death), while the negative-predictive-value significantly exceeded 90% for all analyses. The utilization of and costs for emergency transport as well as emergency and any hospital visits as well as the utilization of conventional outpatient care and laboratory services were consistently found most relevant for predicting mortality. All models showed useful accuracies, and more complex models showed advantages. The variables employed for prediction were consistent across models and with medical reasoning. Identifying individuals at risk could assist tailored decision-making and interventions

Machine Learning in Dentistry: A Scoping Review

Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies

Impact of Noisy Labels on Dental Deep Learning—Calculus Detection on Bitewing Radiographs

Supervised deep learning requires labelled data. On medical images, data is often labelled inconsistently (e.g., too large) with varying accuracies. We aimed to assess the impact of such label noise on dental calculus detection on bitewing radiographs. On 2584 bitewings calculus was accurately labeled using bounding boxes (BBs) and artificially increased and decreased stepwise, resulting in 30 consistently and 9 inconsistently noisy datasets. An object detection network (YOLOv5) was trained on each dataset and evaluated on noisy and accurate test data. Training on accurately labeled data yielded an mAP50: 0.77 (SD: 0.01). When trained on consistently too small BBs model performance significantly decreased on accurate and noisy test data. Model performance trained on consistently too large BBs decreased immediately on accurate test data (e.g., 200% BBs: mAP50: 0.24; SD: 0.05; p < 0.05), but only after drastically increasing BBs on noisy test data (e.g., 70,000%: mAP50: 0.75; SD: 0.01; p < 0.05). Models trained on inconsistent BB sizes showed a significant decrease of performance when deviating 20% or more from the original when tested on noisy data (mAP50: 0.74; SD: 0.02; p < 0.05), or 30% or more when tested on accurate data (mAP50: 0.76; SD: 0.01; p < 0.05). In conclusion, accurate predictions need accurate labeled data in the training process. Testing on noisy data may disguise the effects of noisy training data. Researchers should be aware of the relevance of accurately annotated data, especially when testing model performances

Patients’ Perspectives on Artificial Intelligence in Dentistry: A Controlled Study

Background: As artificial intelligence (AI) becomes increasingly important in modern dentistry, we aimed to assess patients' perspectives on AI in dentistry specifically for radiographic caries detection and the impact of AI-based diagnosis on patients' trust. Methods: Validated questionnaires with Likert-scale batteries (1: "strongly disagree" to 5: "strongly agree") were used to query participants' experiences with dental radiographs and their knowledge/attitudes towards AI as well as to assess how AI-based communication of a diagnosis impacted their trust, belief, and understanding. Analyses of variance and ordinal logistic regression (OLR) were used (p < 0.05). Results: Patients were convinced that "AI is useful" (mean Likert +/- standard deviation 4.2 +/- 0.8) and did not fear AI in general (2.2 +/- 1.0) nor in dentistry (1.6 +/- 0.8). Age, education, and employment status were significantly associated with patients' attitudes towards AI for dental diagnostics. When shown a radiograph with a caries lesion highlighted by an arrow, patients recognized the lesion significantly less often than when using AI-generated coloured overlays highlighting the lesion (p < 0.0005). AI-based communication did not significantly affect patients' trust in dentists' diagnosis (p = 0.44; OLR). Conclusions: Patients showed a positive attitude towards AI in dentistry. AI-supported diagnostics may assist communicating radiographic findings by increasing patients' ability to recognize caries lesions on dental radiographs

Costs for Statutorily Insured Dental Services in Older Germans 2012–2017

Objectives: We assessed the costs of dental services in statutorily insured, very old (geriatric) Germans. Methods: A comprehensive sample of very old (≥75 years) people insured at a large Northeastern statutory insurer was followed over 6 years (2012–2017). We assessed dental services costs for: (1) examination, assessments and advice, (2) operative, (3) surgical, (4) prosthetic, (5) periodontal, (6) preventive and (7) outreach services. Association of utilization with: (1) sex, (2) age, (3) region, (4) social hardship status, (5) International Disease Classification (ICD-10) diagnoses and (6) Diagnoses Related Groups (DRGs) was explored. Results: 404,610 individuals with a mean (standard deviation, SD) age 81.9 (5.4 years) were followed, 173,733 did not survive follow-up. Total mean costs were 129.61 (310.97) euro per capita; the highest costs were for prosthetic (54.40, SD 242.89 euro) and operative services (28.40, SD 68.38 euro), examination/advice (21.15, SD 28.77 euro), prevention (13.31, SD 49.79 euro), surgery (5.91, SD 23.91 euro), outreach (4.81, SD 28.56 euro) and periodontal services (1.64, SD 7.39 euro). The introduction of new fee items for outreach and preventive services between 2012 and 2017 was reflected in costs. Total costs decreased with increasing age, and this was also found for all service blocks except outreach and preventive services. Costs were higher in those with social hardship status, and in Berlin than Brandenburg and Mecklenburg-Western Pomerania. Certain general health conditions were associated with increased or decreased costs. Conclusions: Costs were associated with sex, social hardship status, place of living and general health conditions. Clinical significance: Dental services costs for the elderly in Germany are unequally distributed and, up to a certain age or health status, generated by invasive interventions mainly. Policy makers should incentivize preventive services earlier on and aim to distribute expenses more equally